Get the latest New Orleans weather forecast, news, and alerts from the WGNO weather team. See the latest episode of Weather &amp; Science for Kids.

Rain ending Friday evening but more on the way this weekend

New Orleans Weather & 7 Day Forecast | WGNO.com

Any storms that develop may have brief heavy downpours and gusty winds.

More storms for New Orleans late Friday through Saturday

Expect another warm day on Friday in Northern California, but a weekend cooldown is coming.

Northern California forecast: 90s on Friday ahead of a cooler weekend

Dramatic mapping shows spirals of wind and rain circling over the Shetland Islands and the North Sea - with heavy downpours set to pummel the UK into next week

UK summer forecast: Country split in HALF as Arctic storms send temperatures tumbling in parts of Britain

Severe thunderstorms packing damaging winds of up to 60 mph and heavy rains are possible across Louisiana Friday afternoon and evening, followed by scattered storms that should continue through the

Severe weather is possible across Louisiana this weekend. See forecast for your city.

According to the India Meteorological Department (IMD), the weather forecast indicates a mainly clear sky, gradually transitioning to partly cloudy conditions in the afternoon or evening. Temperatures are expected to peak at 38 degrees Celsius, with a minimum temperature hovering around 24 ...

Pune Weather: City To Witness Thunderstorms, Rain From June 3 | Check Weekly Forecast | Times Now

Bay Area Heat Wave Could Push Temps To Triple Digits: Forecast - San Francisco, CA - The first heat wave of the season is on the horizon, and it looks like a scorcher. Here&#x27;s what to know.

Bay Area Heat Wave Could Push Temps To Triple Digits: Forecast | San Francisco, CA Patch

It's one of auto racing's biggest days of the year, and weather will likely be a factor. - Articles from The Weather Channel | weather.com

Daytona 500 Forecast: Rain Likely Sunday | Weather.com

Up to several inches could pile up overnight into early Saturday; the snow could fall quite heavily for a time.

D.C.-area forecast: Increasing clouds ahead of accumulating snow tonight - The Washington Post

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TORTOLA, British Virgin Islands, December 17th, 2024/Chainwire/--According to the prediction of Ransu Salovaara, CEO of TokenBuilder and a crypto veteran since 2014, the next twelve months will be pivotal in boosting transparency for token launches and integrating AI technology into new crypto projects. Salovaara, who helped raise token sales for major names in the industry, believes 2025 will be dominated by two major trends: Fair Token Offerings (FTOs) — A push for more reasonable valuations and more equitable access for everyday investors. AI Tokens — A major leap forward where crypto meets advanced AI agents, opening fresh opportunities for projects and investors alike. “2024 was phenomenal, with Bitcoin hitting $100,000 on December 4th—CoinMarketCap confirms this milestone,” says Salovaara. “Thousands of meme-coins also launched this year, many reaching secondary markets, as tracked by BDC Consulting. But 2025 is set to be even bigger. I predict Bitcoin will push towards $150k–$200k, and meme-coins will hopefully adopt stricter compliance. Even more exciting is how AI is finally converging with crypto, spawning a new generation of AI-driven utility tokens.” Fair Token Offerings (FTOs): Leveling the Playing Field While Bitcoin’s and meme-coin's rise dominated headlines in 2024, these trends also highlighted how rampant scams and rug-pulls remain. Meme-coins in particular saw thousands of launches, not all created equal. Private insiders often orchestrated low-float, high-valuation tactics—leaving retail investors at a disadvantage. To counter these, TokenBuilder has pioneered an FTO Rating system, providing a transparent scoring framework for new token offerings. This rating focuses on five pillars: Access: Retail investor inclusion in private rounds Appreciation: Fair token valuations with upside potential Supply: At least 20% circulating supply after token launch to avoid price manipulation Liquidity: Early secondary market trading plans (DEX or otherwise) Transparency: Public, identifiable teams rather than anonymous founders “We want to end the days of anonymous projects,” Salovaara says. “A legitimate project should be proud of its team, structure, and open to scrutiny.” FTOcalendar.com is TokenBuilder’s curated platform listing upcoming offerings. Each token must achieve at least one star on the FTO Rating scale to even be considered—raising the bar for fairness in Web3 fundraising. Although the five-pillar overview remains the core, TokenBuilder continues refining the FTO Rating for greater sophistication. AI Tokens: The Next Billion-Dollar Crypto Sector According to Salovaara, For most of 2024, AI was overshadowed by surging meme-coins and the mainstream adoption of Bitcoin ETFs. Yet behind the scenes, developers have been blending language models (OpenAI, Grog, Llama) with crypto utility. In practice, these AI tokens will: Use AI-agent frameworks (like AI16Z’s “Eliza”) to enable advanced, automated services Offer exclusive data and analytics functionalities Function as native payment methods for discounted or premium AI services “We see AI tokens as the next wave of utility tokens—where owning them isn’t just about price speculation, but unlocking actual AI-based features,” Salovaara explains. “Expect many new AI-token launches by mid-2025, each aiming to monetize everything from data analytics to real-time predictions. We will launch our first version of ‘Eliza’ in January–February 2025.” TokenBuilder’s Roadmap for 2025 and Beyond Building on a decade of crypto experience, TokenBuilder is developing a Fair Token Offering launchpad focused on AI-driven tokens. Using on-chain sales accessible to everyone, the platform plans to offer: $TBAI launch on Uniswap 2nd of January 2025 Multiple token offering methods: Fixed-price sales, Liquidity Bootstrapping Pool (LBP) auctions, and more Multi-chain support: Ethereum, Base, and possibly other Ethereum Layer-2 networks AI-Powered Tools: AI-powered tokenomics analysis, compliance tracking, and advanced analytics Alongside the FTO Calendar and an “AI-Agent Army” for compliance and tokenization, TokenBuilder aims to streamline the entire process of launching new tokens in a fair, open, and transparent manner. “We believe the ‘AI-Agent Army’ will be central to many utility tokens from 2025 onward,” Salovaara adds. “Imagine an AI-powered framework that automates compliance checks, optimizes liquidity, and offers real-time market intelligence for both investors and founders. That’s the future we’re building.” Contacts TokenBuilder Platform: tokenbuilder.ai FTO Calendar: FTOcalendar.com Media Contact Ransu Salovaara, Founder & CEO Telegram: @ransu_salovaara Email: ransu@tokenbuilder.ai LinkedIn: linkedin.com/in/ransu About TokenBuilder TokenBuilder is a Fair Token Offering launchpad and platform for Web3 projects. Headquartered in the British Virgin Islands (BVI) as TokenBuilder Ltd. and operating out of Valbonne, France for MiCA-regulated Europe, TokenBuilder combines AI-driven tools and fair, on-chain token sales to help startups and investors navigate the ever-evolving crypto landscape. BVI regulations allow independent token frameworks, closely aligned with standards from England and Wales. Press inquiries and partnership requests are welcome. For more information, users can visit tokenbuilder.ai and FTOcalendar.com Contact CEO Ransu Salovaara TokenBuilder Ltd. ransu@tokenbuilder.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here TORTOLA, British Virgin Islands, December 17th, 2024/Chainwire/--According to the prediction of Ransu Salovaara, CEO of TokenBuilder and a crypto veteran since 2014, the next twelve months will be pivotal in boosting transparency for token launches and integrating AI technology into new crypto projects. Salovaara, who helped raise token sales for major names in the industry, believes 2025 will be dominated by two major trends: Fair Token Offerings (FTOs) — A push for more reasonable valuations and more equitable access for everyday investors. Fair Token Offerings AI Tokens — A major leap forward where crypto meets advanced AI agents, opening fresh opportunities for projects and investors alike. AI Tokens “2024 was phenomenal, with Bitcoin hitting $100,000 on December 4th—CoinMarketCap confirms this milestone,” says Salovaara. “2024 was phenomenal, with Bitcoin hitting $100,000 on December 4th—CoinMarketCap confirms this milestone,” says Salovaara. “Thousands of meme-coins also launched this year, many reaching secondary markets, as tracked by BDC Consulting. But 2025 is set to be even bigger. I predict Bitcoin will push towards $150k–$200k, and meme-coins will hopefully adopt stricter compliance. Even more exciting is how AI is finally converging with crypto, spawning a new generation of AI-driven utility tokens.” “Thousands of meme-coins also launched this year, many reaching secondary markets, as tracked by BDC Consulting. But 2025 is set to be even bigger. I predict Bitcoin will push towards $150k–$200k, and meme-coins will hopefully adopt stricter compliance. Even more exciting is how AI is finally converging with crypto, spawning a new generation of AI-driven utility tokens.” Fair Token Offerings (FTOs): Leveling the Playing Field While Bitcoin’s and meme-coin's rise dominated headlines in 2024, these trends also highlighted how rampant scams and rug-pulls remain. Meme-coins in particular saw thousands of launches, not all created equal. Private insiders often orchestrated low-float, high-valuation tactics—leaving retail investors at a disadvantage. Fair Token Offerings (FTOs): To counter these, TokenBuilder has pioneered an FTO Rating system, providing a transparent scoring framework for new token offerings. This rating focuses on five pillars: Access : Retail investor inclusion in private rounds Access Appreciation: Fair token valuations with upside potential Appreciation: Supply: At least 20% circulating supply after token launch to avoid price manipulation Supply: Liquidity: Early secondary market trading plans (DEX or otherwise) Liquidity: Transparency: Public, identifiable teams rather than anonymous founders Transparency: “We want to end the days of anonymous projects,” Salovaara says. “A legitimate project should be proud of its team, structure, and open to scrutiny.” “We want to end the days of anonymous projects,” Salovaara says. “A legitimate project should be proud of its team, structure, and open to scrutiny.” FTOcalendar.com is TokenBuilder’s curated platform listing upcoming offerings. Each token must achieve at least one star on the FTO Rating scale to even be considered—raising the bar for fairness in Web3 fundraising. Although the five-pillar overview remains the core, TokenBuilder continues refining the FTO Rating for greater sophistication. FTOcalendar.com AI Tokens: The Next Billion-Dollar Crypto Sector According to Salovaara, For most of 2024, AI was overshadowed by surging meme-coins and the mainstream adoption of Bitcoin ETFs. Yet behind the scenes, developers have been blending language models (OpenAI, Grog, Llama) with crypto utility. In practice, these AI tokens will: Use AI-agent frameworks (like AI16Z’s “Eliza”) to enable advanced, automated services Offer exclusive data and analytics functionalities Function as native payment methods for discounted or premium AI services AI Tokens: The Next Billion-Dollar Crypto Sector According to Salovaara, For most of 2024, AI was overshadowed by surging meme-coins and the mainstream adoption of Bitcoin ETFs. Yet behind the scenes, developers have been blending language models (OpenAI, Grog, Llama) with crypto utility. In practice, these AI tokens will: Use AI-agent frameworks (like AI16Z’s “Eliza”) to enable advanced, automated services Offer exclusive data and analytics functionalities Function as native payment methods for discounted or premium AI services “We see AI tokens as the next wave of utility tokens—where owning them isn’t just about price speculation, but unlocking actual AI-based features,” Salovaara explains. “Expect many new AI-token launches by mid-2025, each aiming to monetize everything from data analytics to real-time predictions. We will launch our first version of ‘Eliza’ in January–February 2025.” “We see AI tokens as the next wave of utility tokens—where owning them isn’t just about price speculation, but unlocking actual AI-based features,” Salovaara explains. “Expect many new AI-token launches by mid-2025, each aiming to monetize everything from data analytics to real-time predictions. We will launch our first version of ‘Eliza’ in January–February 2025.” TokenBuilder’s Roadmap for 2025 and Beyond Building on a decade of crypto experience, TokenBuilder is developing a Fair Token Offering launchpad focused on AI-driven tokens. Using on-chain sales accessible to everyone, the platform plans to offer: $TBA I launch on Uniswap 2nd of January 2025 $TBA Multiple token offering methods: Fixed-price sales, Liquidity Bootstrapping Pool (LBP) auctions, and more Multiple token offering methods: Multi-chain support: Ethereum, Base, and possibly other Ethereum Layer-2 networks Multi-chain support: AI-Powered Tools: AI-powered tokenomics analysis, compliance tracking, and advanced analytics AI-Powered Tools: Alongside the FTO Calendar and an “AI-Agent Army” for compliance and tokenization, TokenBuilder aims to streamline the entire process of launching new tokens in a fair, open, and transparent manner. “We believe the ‘AI-Agent Army’ will be central to many utility tokens from 2025 onward,” Salovaara adds. “Imagine an AI-powered framework that automates compliance checks, optimizes liquidity, and offers real-time market intelligence for both investors and founders. That’s the future we’re building.” “We believe the ‘AI-Agent Army’ will be central to many utility tokens from 2025 onward,” Salovaara adds. “Imagine an AI-powered framework that automates compliance checks, optimizes liquidity, and offers real-time market intelligence for both investors and founders. That’s the future we’re building.” Contacts TokenBuilder Platform: tokenbuilder.ai FTO Calendar: FTOcalendar.com Media Contact Ransu Salovaara, Founder & CEO Telegram: @ransu_salovaara Email: ransu@tokenbuilder.ai LinkedIn: linkedin.com/in/ransu tokenbuilder.ai FTOcalendar.com ransu@tokenbuilder.ai linkedin.com/in/ransu About TokenBuilder TokenBuilder is a Fair Token Offering launchpad and platform for Web3 projects. Headquartered in the British Virgin Islands (BVI) as TokenBuilder Ltd. and operating out of Valbonne, France for MiCA-regulated Europe, TokenBuilder combines AI-driven tools and fair, on-chain token sales to help startups and investors navigate the ever-evolving crypto landscape. BVI regulations allow independent token frameworks, closely aligned with standards from England and Wales. Press inquiries and partnership requests are welcome. For more information, users can visit tokenbuilder.ai and FTOcalendar.com tokenbuilder.ai FTOcalendar.com Contact CEO Ransu Salovaara TokenBuilder Ltd. ransu@tokenbuilder.ai ransu@tokenbuilder.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

TokenBuilder Releases 2025 Forecast On AI Integration And Fair Token Offerings

TokenBuilder ukax 2025 maran Pronóstico AI Integración Ukat Chiqap Token Ofertas ukanakat uñt’ayi

TokenBuilder публікуе прагноз на 2025 год па інтэграцыі штучнага інтэлекту і справядлівым прапановам токенаў

টোকেনবিল্ডার এআই ইন্টিগ্রেশন এবং ন্যায্য টোকেন অফারিং সম্পর্কে 2025 পূর্বাভাস প্রকাশ করেছে

TokenBuilder veröffentlicht Prognose für 2025 zur KI-Integration und fairen Token-Angeboten

TokenBuilder publica pronóstico para 2025 sobre integración de IA y ofertas justas de tokens

TokenBuilder、AI統合と公正なトークン提供に関する2025年の予測を発表

TokenBuilder E Lokolla Ponelopele ya 2025 Ka Kopanyo ya AI Le Ditlhagišo tša Token tše di Lokilego

TokenBuilder publikuje prognozę na rok 2025 dotyczącą integracji AI i uczciwych ofert tokenów

TokenBuilder wuxuu siidaayaa Saadaasha 2025 ee Is-dhexgalka AI iyo Bixinta Token cadaalad ah

TokenBuilder ትንበያ 2025 ኣብ ውህደት AIን ፍትሓዊ ምቕራብ ቶከንን ኣውጺኡ

Ukukhutshwa kweTokenBuilder ngo-2025 kuHlaziyo lwe-AI kunye neMithetho yeMifanekiso efanelekileyo

Ukukhishwa kwe-TokenBuilder 2025 Isibikezelo Sokuhlanganisa I-AI kanye Neminikelo Yethokheni Efanelekile

Author:
(1) David Staines. Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility This part presents evidence on the volatility of trend inflation. The focus here is on time series ideas. It matters how you de-trend the data. There is significant low frequency volatility in inflation, over typical sample periods. Contrast the Great Depression with the Great Inflation and think of Japan’s lost decades. Under inflation targeting it is natural to think of the headline target as the trend inflation rate. This makes trend inflation much less volatile. Nevertheless, there has been discussion about changing the inflation target which should be factored in by rational agents, (see for example Blanchard et al. [2010], Ball [2013] and Cecchetti and Schoenholtz [2017]). The European Central Bank’s recent move to a symmetric inflation target might work out like this (Ignazio and Gros [2021]). The asymptotic justification is a little delicate though. As shocks shrink, are trend inflation shocks sufficiently large to be considered first order? Typically, trend inflation is not observed but estimated via a statistical decomposition. It is difficult to get away from the influence of tuning parameters set a priori. In the popular Hodrick and Prescott [1997] framework, the quadratic loss function weights deviations in trend λ = 1600 times more than variation in the cycle for quarterly data (Ravn and Uhlig [2002]). This corresponds to a standard deviation ratio of 40, which can be viewed as a Bayesian prior. Take the standard deviation of quarterly output to be 0.5%. This is plausible for advanced economies in normal times. It is roughly consistent with a variety of evidence presented by Cogley and Sargent [2002], Stock and Watson [2002], Stock and Watson [2005], Keating and Valcarcel [2012] and Hulseman and Detmeister [29 July, 2017]. Throughout the subsequent discussion I use asymptotic notation x ≍ y to mean x and y are of the same order of magnitude. I take this to mean that one variable is on average no more than a tenth of the other. [147] Hamilton’s technique is controversial. The literature has long been aware of problems with the standard filter. It generates spurious cycles, uses future information to construct present trend components and does not account for events that might be best considered structural breaks; for more on this view consult Harvey and Jaeger [1993], King and Rebelo [1993], Cogley and Nason [1995], Canova [1998], Baxter and King [1999] and Christiano and Fitzgerald [2003]. Nevertheless, recent work by Hodrick [2020] shows that his filter outperforms others, when there are slow-moving changes in persistent components which might be a plausible consequence of, for example, long-run structural change. [148] This calibration exercise and surrounding discussion have justified the claim that there is no decisive empirical answer to whet her shocks to trend inflation are first order or not. This should be an important field for future work. We are in a far better place than before as a discipline, if we are discussing the econometric implementation of our main model, rather than debating its underlying contents. This paper is available on arxiv under CC 4.0 license. Author: (1) David Staines. Author: Author: (1) David Staines. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility This part presents evidence on the volatility of trend inflation. The focus here is on time series ideas. It matters how you de-trend the data. There is significant low frequency volatility in inflation, over typical sample periods. Contrast the Great Depression with the Great Inflation and think of Japan’s lost decades. Under inflation targeting it is natural to think of the headline target as the trend inflation rate. This makes trend inflation much less volatile. Nevertheless, there has been discussion about changing the inflation target which should be factored in by rational agents, (see for example Blanchard et al. [2010], Ball [2013] and Cecchetti and Schoenholtz [2017]). The European Central Bank’s recent move to a symmetric inflation target might work out like this (Ignazio and Gros [2021]). The asymptotic justification is a little delicate though. As shocks shrink, are trend inflation shocks sufficiently large to be considered first order? Typically, trend inflation is not observed but estimated via a statistical decomposition. It is difficult to get away from the influence of tuning parameters set a priori. In the popular Hodrick and Prescott [1997] framework, the quadratic loss function weights deviations in trend λ = 1600 times more than variation in the cycle for quarterly data (Ravn and Uhlig [2002]). This corresponds to a standard deviation ratio of 40, which can be viewed as a Bayesian prior. Take the standard deviation of quarterly output to be 0.5%. This is plausible for advanced economies in normal times. It is roughly consistent with a variety of evidence presented by Cogley and Sargent [2002], Stock and Watson [2002], Stock and Watson [2005], Keating and Valcarcel [2012] and Hulseman and Detmeister [29 July, 2017]. Throughout the subsequent discussion I use asymptotic notation x ≍ y to mean x and y are of the same order of magnitude. I take this to mean that one variable is on average no more than a tenth of the other. [147] Hamilton’s technique is controversial. The literature has long been aware of problems with the standard filter. It generates spurious cycles, uses future information to construct present trend components and does not account for events that might be best considered structural breaks; for more on this view consult Harvey and Jaeger [1993], King and Rebelo [1993], Cogley and Nason [1995], Canova [1998], Baxter and King [1999] and Christiano and Fitzgerald [2003]. Nevertheless, recent work by Hodrick [2020] shows that his filter outperforms others, when there are slow-moving changes in persistent components which might be a plausible consequence of, for example, long-run structural change. [148] This calibration exercise and surrounding discussion have justified the claim that there is no decisive empirical answer to whet her shocks to trend inflation are first order or not. This should be an important field for future work. We are in a far better place than before as a discipline, if we are discussing the econometric implementation of our main model, rather than debating its underlying contents. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Why Trend Inflation Volatility is Key for Future Economic Forecasts

What is the one thing we all need to ask before we leave the house each morning? “What’s the weather going to be like today?” Luckily, we live in the transformational age of advanced and ever-evolving technology, and we now have the gift of artificial intelligence and machine learning (ML). By looking at years of weather data and pairing it with known weather patterns and outside factors, computers can now predict the weather like never before. For instance, during the Paris Olympics this year, AI tools were used to provide a daily forecast of weather conditions. Recent advances in ML-based weather prediction (MLWP) have produced ML-based models with less forecast error than simpler past models. As noted by the science news magazine EOS, “It took fifty years to build and incrementally improve the sophisticated computer models needed to produce such accurate forecasts. It has taken ML approaches just a couple of years to match, and in some cases surpass, the skill of these traditional models.” Now, Google DeepMind scientists have developed a weather model that largely beats the world’s most accurate modeling system. Researchers believe the model may be able to accurately predict unprecedented events that climate change accentuates. According to a report last month commissioned by the International Chamber of Commerce, “climate-related extreme weather events have cost the global economy more than $2 trillion over the past decade.” According to Maginative, “Google DeepMind’s new high-resolution AI model, GenCast, delivers faster, more accurate weather predictions than traditional methods. AI will fundamentally change how we plan for everyday weather and extreme events.” Part of the reason why this is such a significant development is because GenCast offers weather forecasts that are not only more reliable but also faster. Unlike traditional methods that rely on supercomputers and take hours, GenCast runs on a single TPU chip in just eight minutes, predicting a wide range of possible outcomes rather than a single scenario, as current weather prediction models do. In contrast to traditional models, which make forecasts by solving physics equations, GenCast learns directly from historical weather data. Jacob Mallinder, in CEO Today, outlines some of the top AI innovations that are making a difference in weather forecasting today. He explains that aside from improved data gathering and predictive analytics, AI today allows for insight into climate change, agricultural optimization, improving efficiency in the energy sector, and enhancing aviation safety. MAELSTROM is another large-scale R&D project, aiming to fundamentally improve weather and climate prediction. It aims to combine the powers of high-performance computing (HPC) and ML to cope with the extreme complexity inherent in weather and climate forecasts. In August, NVIDIA Research announced a new generative AI model, dubbed StormCast, for emulating high-fidelity atmospheric dynamics. The model was built to provide reliable weather prediction at the mesoscale — a scale larger than storms but smaller than cyclones — which is critical for disaster planning and mitigation. Quantum ML is another option, but some experts believe it is not the optimal way to go as quantum computing is still in the early stages of development. If it is the case that GenCast and other emerging technologies can predict serious weather events, it will introduce a monumental change. With such abilities, we can warn people ahead of time and prevent traffic accidents. Farmers can plan their farming and prevent crop loss. Energy companies will be able to run more effectively if they can understand sun and wind patterns in advance. And when we walk out the door, we won’t need to wonder what the weather will be like because we’ll already know. What is the one thing we all need to ask before we leave the house each morning? “What’s the weather going to be like today?” Luckily, we live in the transformational age of advanced and ever-evolving technology, and we now have the gift of artificial intelligence and machine learning (ML). By looking at years of weather data and pairing it with known weather patterns and outside factors, computers can now predict the weather like never before. For instance, during the Paris Olympics this year, AI tools were used to provide a daily forecast of weather conditions. Recent advances in ML-based weather prediction (MLWP) have produced ML-based models with less forecast error than simpler past models. less forecast error As noted by the science news magazine EOS , “It took fifty years to build and incrementally improve the sophisticated computer models needed to produce such accurate forecasts. It has taken ML approaches just a couple of years to match, and in some cases surpass, the skill of these traditional models.” noted EOS Now, Google DeepMind scientists have developed a weather model that largely beats the world’s most accurate modeling system. weather model Researchers believe the model may be able to accurately predict unprecedented events that climate change accentuates. According to a report last month commissioned by the International Chamber of Commerce , “climate-related extreme weather events have cost the global economy more than $2 trillion over the past decade.” commissioned by the International Chamber of Commerce According to Maginative , “Google DeepMind’s new high-resolution AI model, GenCast, delivers faster, more accurate weather predictions than traditional methods. AI will fundamentally change how we plan for everyday weather and extreme events.” According to Maginative Part of the reason why this is such a significant development is because GenCast offers weather forecasts that are not only more reliable but also faster. Unlike traditional methods that rely on supercomputers and take hours, GenCast runs on a single TPU chip in just eight minutes, predicting a wide range of possible outcomes rather than a single scenario, as current weather prediction models do. In contrast to traditional models, which make forecasts by solving physics equations, GenCast learns directly from historical weather data. Jacob Mallinder, in CEO Today, outlines some of the top AI innovations that are making a difference in weather forecasting today. outlines some of the top AI innovations He explains that aside from improved data gathering and predictive analytics, AI today allows for insight into climate change, agricultural optimization, improving efficiency in the energy sector, and enhancing aviation safety. MAELSTROM is another large-scale R&D project , aiming to fundamentally improve weather and climate prediction. It aims to combine the powers of high-performance computing (HPC) and ML to cope with the extreme complexity inherent in weather and climate forecasts. large-scale R&D project In August, NVIDIA Research announced a new generative AI model , dubbed StormCast, for emulating high-fidelity atmospheric dynamics. announced a new generative AI model The model was built to provide reliable weather prediction at the mesoscale — a scale larger than storms but smaller than cyclones — which is critical for disaster planning and mitigation. Quantum ML is another option , but some experts believe it is not the optimal way to go as quantum computing is still in the early stages of development. Quantum ML is another option If it is the case that GenCast and other emerging technologies can predict serious weather events, it will introduce a monumental change. With such abilities, we can warn people ahead of time and prevent traffic accidents. Farmers can plan their farming and prevent crop loss. Energy companies will be able to run more effectively if they can understand sun and wind patterns in advance. And when we walk out the door, we won’t need to wonder what the weather will be like because we’ll already know.

Weather Forecasting Through Machine Learning

Steal My Subscription, Revenue Forecasting Model (P.S.: It Tracks Churn!)

The HackerNoon Newsletter: Polymarket Explained: How Blockchain Prediction Markets Are Shaping the Future of Forecasting (11/9/2024)

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Polymarket обяснява: Как пазарите за прогнозиране на блокчейн оформят бъдещето на прогнозирането

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What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here. In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court—will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. https://twitter.com/Guntar/status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild (@dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article. And @Angry_Staffer, based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog, and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election. I also post regular updates on Bluesky and Twitter. ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ What if pollsters are just too afraid of Trump this time around? What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here . own little forecast half here here In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court —will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. recently allowed by the Supreme Court In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Mason McCann Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. very https://twitter.com/ Guntar /status/1851339376196010260?embedable=true https://twitter.com/ Guntar Guntar /status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild ( @dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” @dcg1114 https://twitter.com/dcg1114/status/1851779439899578606?embedable=true https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article . And @Angry_Staffer , based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” his latest article @Angry_Staffer https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog , and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. analyzing and providing past polling biases on his blog As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: my Monte Carlo simulation ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. the In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election . I also post regular updates on Bluesky and Twitter . https://github.com/maxspeicher/2024-us-presidential-election Bluesky Twitter ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ ☕ I love coffee, and if you want to support my work, you can always send me one , or subscribe to my newsletter . 🗞️ send me one send me one my newsletter my newsletter

Election Forecasts, Schmelection Forecasts

Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it. Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it.

What Could the Future of Weather Forecasting Look Like?

Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5%, according to the U.S. Bureau of Labor Statistics. AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __process complex data in milliseconds __and update it whenever they receive new information. A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation. Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. rely on statistical models rely on statistical models Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5% , according to the U.S. Bureau of Labor Statistics. would rise by 7.5% would rise by 7.5% actually increased by 6.5% actually increased by 6.5% AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. up to 70% of the workday up to 70% of the workday Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __ process complex data in milliseconds __and update it whenever they receive new information. process complex data in milliseconds A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. passed at least 15 bills passed at least 15 bills Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation.

The Role of AI in Forecasting Economic Trends

Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References A Appendix 3 Forecasting with the TC-TVP-VAR In this section I consider the problem of forecasting the rate of growth of GDP, the inflation rate and the Fed Fund rate using a trivariate TVP-VAR model. In the specific, I estimate a trivariate TVP-VAR for the US economy using data from 1970 up to 2019 and I compare the forecast accuracy of a standard TVP-VAR model to the forecasts from a TC-TVP-VAR. 3.1 Small scale New Keynesian model for the US economy In the TC-TVP-VAR I exploit the New Keynesian model in Del Negro et al. (2004) to parametrize the theory coherent prior. The conceptual framework commonly denoted as the 3-equation New Keynesian model constitutes the nucleus of Michael Woodford’s book “Interest and Prices” (Woodford 2003) and underpins most of modern monetary macroeconomics models.[21] More specifically, the structural model is composed by an IS curve (31), a New Keynesian Phillips curve (32), a monetary policy rule (33) and two equations that describe the dynamics for the log-deviation from the steady state of technological process (34) and government spending (35), namely The population moments needed to parametrize the prior are derived from the state-space representation of the New Keynesian model obtained by solving the system of non-linear rational expectation equations. In particular, the non-linear rational expectation equations are solved using the method based on matrix eigenvalue decomposition by Sims (2002) leading to a solution which has the form 3.2 Forecast comparison Table 1 shows the comparison of the forecasts from a standard TVP-VAR model and the TCTVP-VAR model. The forecasting exercise is designed such that I compute the recursive one quarter, two quarters, and one year ahead forecasts starting from 1985-Q1 up to 2019-Q4.22 To compare relative point forecast accuracy, in the table I report the Root Mean Squared Error (RMSE) while for evaluating density forecast accuracy I report the average Cumulative Ranked Probability Scores (CRPS). In the table I also include the results concerning the forecasts from a constant parameters VAR with flat prior and a constant parameters Bayesian VAR (BVAR) with Minnesota type of prior.23 Overall, the TC-TVP-VAR provides the most accurate point and density forecasts for both output growth and inflation rate, outperforming the standard TVP-VAR model at all the horizons considered. For forecasting output growth, the standard TVP-VAR performs poorly relative to the TC-TVP-VAR, but also to the constant parameters BVAR with Minnesota prior, suggesting that the model tends to fit some noise in the time series of output growth. In line with the previous forecasting literature, allowing for time variation of the parameters of the VAR is important for obtaining accurate forecasts of the inflation rate, as the standard TVP-VAR outperforms both the VAR with flat prior and the BVAR with the Minnesota prior. However, economic shrinkage is helpful to obtain more reliable point and density forecasts when modelling the time variation of the coefficients. Indeed the TC-TVP-VAR outperforms the standard TVP-VAR at all the horizons. As a caveat, the standard Minnesota prior centering the autoregressive coefficients on a random walk process outperforms the other competitors, including the TC-TVP-VAR, for forecasting the Fed Fund Rate. This result is consistent with the results of the forecasting exercise in Del Negro et al. (2004) which use the same small scale NK model to parametrize a prior for a constant parameters VAR. This paper is available on arxiv under CC 4.0 license. [21.] See Del Negro et al. (2004) for the more in depth details on the New Keynesian Model. [22.] Details on the data can be found in the appendix A.2.1 [23.] Appendix A.2.2 the reports details on the competing forecasting models Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Abstract and Introduction Theory coherent TVP-VAR Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References Conclusion and References A Appendix A Appendix 3 Forecasting with the TC-TVP-VAR In this section I consider the problem of forecasting the rate of growth of GDP, the inflation rate and the Fed Fund rate using a trivariate TVP-VAR model. In the specific, I estimate a trivariate TVP-VAR for the US economy using data from 1970 up to 2019 and I compare the forecast accuracy of a standard TVP-VAR model to the forecasts from a TC-TVP-VAR. 3.1 Small scale New Keynesian model for the US economy In the TC-TVP-VAR I exploit the New Keynesian model in Del Negro et al. (2004) to parametrize the theory coherent prior. The conceptual framework commonly denoted as the 3-equation New Keynesian model constitutes the nucleus of Michael Woodford’s book “Interest and Prices” (Woodford 2003) and underpins most of modern monetary macroeconomics models.[21] More specifically, the structural model is composed by an IS curve (31), a New Keynesian Phillips curve (32), a monetary policy rule (33) and two equations that describe the dynamics for the log-deviation from the steady state of technological process (34) and government spending (35), namely The population moments needed to parametrize the prior are derived from the state-space representation of the New Keynesian model obtained by solving the system of non-linear rational expectation equations. In particular, the non-linear rational expectation equations are solved using the method based on matrix eigenvalue decomposition by Sims (2002) leading to a solution which has the form 3.2 Forecast comparison Table 1 shows the comparison of the forecasts from a standard TVP-VAR model and the TCTVP-VAR model. The forecasting exercise is designed such that I compute the recursive one quarter, two quarters, and one year ahead forecasts starting from 1985-Q1 up to 2019-Q4.22 To compare relative point forecast accuracy, in the table I report the Root Mean Squared Error (RMSE) while for evaluating density forecast accuracy I report the average Cumulative Ranked Probability Scores (CRPS). In the table I also include the results concerning the forecasts from a constant parameters VAR with flat prior and a constant parameters Bayesian VAR (BVAR) with Minnesota type of prior.23 Overall, the TC-TVP-VAR provides the most accurate point and density forecasts for both output growth and inflation rate, outperforming the standard TVP-VAR model at all the horizons considered. For forecasting output growth, the standard TVP-VAR performs poorly relative to the TC-TVP-VAR, but also to the constant parameters BVAR with Minnesota prior, suggesting that the model tends to fit some noise in the time series of output growth. In line with the previous forecasting literature, allowing for time variation of the parameters of the VAR is important for obtaining accurate forecasts of the inflation rate, as the standard TVP-VAR outperforms both the VAR with flat prior and the BVAR with the Minnesota prior. However, economic shrinkage is helpful to obtain more reliable point and density forecasts when modelling the time variation of the coefficients. Indeed the TC-TVP-VAR outperforms the standard TVP-VAR at all the horizons. As a caveat, the standard Minnesota prior centering the autoregressive coefficients on a random walk process outperforms the other competitors, including the TC-TVP-VAR, for forecasting the Fed Fund Rate. This result is consistent with the results of the forecasting exercise in Del Negro et al. (2004) which use the same small scale NK model to parametrize a prior for a constant parameters VAR. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [21.] See Del Negro et al. (2004) for the more in depth details on the New Keynesian Model. [22.] Details on the data can be found in the appendix A.2.1 [23.] Appendix A.2.2 the reports details on the competing forecasting models

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