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

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

The National Weather Service has issued winter weather advisories for 15 New Jersey counties that take effect at 10 p.m. Friday, but snow may arrive a little later.

N.J. weather: When will snow start falling? Latest forecast for up to 5 inches of snow. - nj.com

ForecastAI

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Startups of The Year 2023 Winners: North America 

– that is the key ingredient to dominating your market and thriving in today’s cutthroat and competitive business ecosystem. As the world spins at an unprecedented rate, we see new brands emerging left and right, coupled with cutting-edge technologies that introduce disruptive shifts in the marketplace. Stay ahead of the game To remain relevant and achieve the coveted top-tier status, businesses must adapt and innovate continuously. Artificial Intelligence (AI) is undeniably a game-changer in this endeavor. Today, AI gets thrown around in every corner of any given industry. From optimizing sales processes, and streamlining operations, to boosting business growth, this tech breakthrough proves to be a cornerstone of innovation that’s here to stay for good. AI is no longer a buzzword; it's a strategic imperative for those looking to thrive, rather than merely survive. Here are 10 useful ways to use AI to accelerate your business: Data-Driven Decision Making Here’s where AI excels the most: gathering, processing, and analyzing data in a millisecond to push business leaders away from guesswork and lean toward data-driven decision-making. It is wise to utilize AI-powered analytics tools in your business processes, from identifying trends, and unlocking customer preferences, to deciphering market insights. In uncovering the treasure trove of your company’s data and transforming them into actionable insights, invest in AI-driven algorithm platforms that elevate conversion rates, optimize customer success, and identify the most promising prospects. A great example is Coho AI, a software that capitalizes on product usage data, intelligent segmentation, and user journey, equipping sales, operations, product, and customer success, with powerful AI-driven insights, and streamlining their processes through intelligent automation. There's an AI for that: Be a Master Dealmaker Dealmaking has evolved from simply reading a room and deciphering your prospects’ body language to a virtual world where you can only see an inch of their frames on your screen. Remote work has certainly paved the way for faster, rapid-fire deals, but has equally introduced a new set of challenges that dealmakers need to navigate through. With the lack of face-to-face interaction, intuitive understanding, and body language cues, dealmaking in the digital age relies heavily on data and technology, making AI an invaluable ally. Invest in behavioral intelligence tools to free yourself from the burden of reading your prospect’s non-verbal cues, empowering you to focus on the core aspects of dealmaking. These tools can analyze communication patterns, identify prospects' preferences, and assess their receptivity to various propositions – so you don’t have to. One tool that makes a difference is Novacy, an AI-powered behavioral intelligence platform, that helps B2B revenue teams close more deals by understanding their prospects' underlying perceptions by using audio, text, and body language analysis. There's an AI for that: Embrace Predictive Maintenance AI has been a saving grace for businesses with physical assets, both in costs and efficiency. By predicting whether a piece of equipment is on the verge of failure or requires maintenance, AI helps prevent costly downtime, extends the lifespan of assets, and reduces maintenance expenditures, ultimately improving the bottom line. Its impact on manufacturing and logistics is multifaceted, driving benefits that extend well beyond the shop floor. *__There's an AI for that: __*Uptake, a leader in predictive maintenance solutions, offers an AI-driven platform that empowers businesses to proactively manage their physical assets. Their software uses machine learning to analyze equipment data, predict maintenance needs, and provide actionable insights. By implementing Uptake's solution, businesses can significantly reduce maintenance costs, enhance asset reliability, and optimize their operations. Improved Marketing Campaigns In the ever-evolving world of marketing, AI can revolutionize your campaigns. AI-driven marketing tools can segment your audience, predict campaign performance, and optimize ad spending in real time. By ensuring your marketing efforts reach the right audience with the right message at the right time, AI can maximize ROI and elevate the impact of your campaigns. Emarsys is a powerful AI marketing platform that enables businesses to create highly personalized and data-driven marketing campaigns. It uses AI to analyze customer behavior, predict future actions, and automate marketing processes, ensuring that each interaction with your audience is highly relevant and effective. There's an AI for that: Supply Chain Optimization The pandemic has proven that supply chain management is a complex task that requires heavy technological support. In an increasingly globalized world, AI bridges this gap by providing real-time insights into your supply chain, from sourcing materials to ensuring on-time deliveries. It optimizes inventory, reduces transportation costs, and improves supply chain efficiency, ultimately impacting your bottom line positively. Leading this new dawn for the supply chain is Coupa, a global player in the continuous supply chain design, which leverages the power of AI and comprehensive data models. Its product, transforms design and analysis from one-off projects to a consistent and repeatable process, enabling businesses to adapt quickly to changing market conditions and reduce costs. There's an AI for that: Supply Chain Modeler, Fraud Detection and Prevention In an age of increasing cyber threats and financial fraud, AI can be an invaluable ally in safeguarding your business. Machine learning algorithms can detect unusual patterns and anomalies, preventing fraudulent transactions and securing your financial assets. An AI-driven fraud detection and prevention platform, Featurespace offers adaptive behavioral analytics to help companies identify and stop fraudulent activities in real time. This proactive tool’s smart technology can protect your business and your customers from financial fraud, which is a must-have in today’s era of data leaks and cybercrimes. There's an AI for that: Personalized Product Recommendations E-commerce businesses can harness AI to deliver personalized product recommendations. By analyzing customers' browsing and purchase history, AI can propose items that are more likely to resonate with individual customers, leading to increased sales and customer satisfaction. When it comes to AI-powered product recommendations, Amazon still leads the pack. As a global e-commerce giant, this platform boasts a cutting-edge recommendation engine that has substantially contributed to its success by providing customers with what they want when they want it. There's an AI for that: Human Resources Optimization AI can streamline various HR processes, from candidate screening and onboarding to employee engagement and performance management. Automation and data-driven insights enable HR professionals to focus on strategic activities, foster a more engaging employee experience, and drive higher retention rates. Workday, an AI-driven HR software, offers tools for talent acquisition, workforce management, and employee engagement. It uses AI to streamline HR processes, improve talent sourcing, and enhance the overall employee experience, ultimately contributing to higher retention rates and a more efficient HR department. : There's an AI for that Risk Management To say that change is constant is an understatement. In the modern business world, change and uncertainty are the norm, exposing companies to risks and vulnerabilities. This makes it high time for businesses to invest in AI-powered risk management solutions where its predictive capabilities can assess market fluctuations and regulatory changes, and enable companies to develop proactive risk mitigation strategies. When it comes to leveraging AI to analyze data from a myriad of sources, forecast potential risks that are often overlooked by humans, and provide actionable insights for decision-makers, RiskWatch is a reliable tool. This comprehensive AI-powered risk assessment management software provides advanced risk assessment and protection against threats that may ruin and tarnish your brand. There's an AI for that: Environmental Sustainability Currently, environmental consciousness is at its peak. From developing sustainable products, and optimizing energy efficiency and consumption to reducing carbon footprint, modern businesses are under increasing pressure to abide by these growing eco-friendly standards. In this eco-conscious era, AI is an indispensable tool that can assist businesses in reducing their environmental impact and embracing sustainability. OneTrust offers ESG and Sustainability Cloud that empowers organizations with the ability to define their core ESG metrics, track progress towards these goals, and foster trust with key stakeholders through enhanced transparency. There's an AI for that: Conclusion As AI technologies continue to advance, their role in business acceleration will only grow. Efficient and effective leaders know that incorporating AI into various operations is paramount to adapt to the ever-changing business landscape, enabling them to stay steps ahead of the game. Embracing AI is no longer a matter of “if” – it’s a matter of “when,” and those who act now will reap its benefits fast.

10 Useful Ways to Use AI to Accelerate Your Business

Many people have discovered ChatGPT’s competencies in code generation. But, how well does it actually perform, and are there alternatives? In this post, I take a look at some scenarios where AI can and the degree to which it can be useful. Finally, I do my best to evaluate its performance. help with coding Intro ChatGPT has been everywhere in the last few months. Reading and hearing about how people use it and ‘force’ it to make mistakes has been fun for a week or two, but it's getting tiring. For some reason, my LinkedIn feed still has at least one "Look how ChatGPT failed"-post every day, and I don't know what people are trying to prove with that. At the same time, I am perplexed by software developers claiming that they are using ChatGPT to do their work. Statements like "This week, I didn't write a single line of code and only used ChatGPT" even made it to podcasts where people discussed which jobs AI might replace in the future. For everyone who has seriously worked as a software developer, it is evident that AI cannot replace software developers soon. Yes, ChatGPT can generate code for a given problem description, but it is not good at solving problems, which is the main task of a software engineer. AI can support specific tasks, though, and I am looking at some examples in this post. AI Pair Programming Several services implement smart code completion, also called AI pair programming. The term is quite misleading and was probably invented by someone without much experience with pair programming. While pair programming is specifically beneficial when two people work together to discuss how something should be implemented, AI [pair programming](https://Pair programming - Wikipedia  Wikipedia https://en.wikipedia.org › wiki › Pair_programming) tools instead help with the next step. As soon as you decide what to implement and where to put the code, it suggests a solution. The two most popular ones are and GitHub Copilot is based on , a model for translating natural language to code based on GPT-3. According to a paper by OpenAI researchers, the model generated working code for given Python docstrings for more than 70% of the attempts. That's quite impressive but still also emphasizes that it requires basic coding skills (i.e., writing a docstring) to retrieve good results. Like ChatGPT, CodeX can also be used for free. GitHub Copilot Tabnine. OpenAI Codex on the OpenAI website I have used Copilot for private projects for quite a while and liked it a lot. In the beginning (when it was still in technical preview), it mainly copied lines from GitHub code. Since then, it has improved and evolved a lot. Especially for code that implements generic tasks (like calculations or conversions), there is a high chance that Copilot suggests a complete function based on a doc string. For example, when you write it would suggest the complete function, including a return value. def reverse(self, x:int) -> int... Copilot also works well for writing tests. Just add the name "test" to a given function name and it does the job. In contrast, Tabnine was a massive disappointment for me, though, and felt like a dumb auto-completion suggesting mostly useless or wrong code. While Copilot uses one large model, Tabnine is based on several smaller models that solve individual problems. This is probably also why it is more convenient to use Tabnine in local mode and train it with your own code (which is not possible with Copilot). Even with a lot of context and well-named variables, the suggestions I received from Tabnine were often far from what you would expect. Using local mode, it worked well in large repositories with a lot of similar code. But this is far too easy compared to the problems I would expect such a model to solve. It was not smart but felt more like a tool for automatic copy and pasting. There might be other use cases and circumstances where Tabnine works well (for example, in huge company code bases where everything is written in a similar style and language) but as someone who is working on many smaller projects, it just didn't work for me. Asking AI to write Code As mentioned in the intro, one of the primary use cases of ChatGPT seems to be code generation. Of course, that was already possible with CodeX, but the boom of ChatGPT made many more people aware of what is possible. Compared to the plugins mentioned before, this approach aims to translate textual problem descriptions into code instead of being integrated into an existing code base. Doing this task is challenging for a model as it involves understanding a problem and generating suitable, functional code that solves this problem. In the past, many models failed to do the former reliably, and even with more specialized models, it still is a considerable challenge that is not solved by ChatGPT. However, let's have a look at the code ChatGPT generates. Asking ChatGPT to generate a weather app I asked ChatGPT to write a Streamlit application that fetches weather data for Dortmund from a free API and displays it in a chart. This is what I got: import streamlit as st
import requests
import pandas as pd
import plotly.express as px

# Set up OpenWeatherMap API key
api_key = "YOUR_API_KEY_HERE"

# Define function to get weather data from OpenWeatherMap API
def get_weather_data():
    url = f"https://api.openweathermap.org/data/2.5/onecall?lat=51.5134&lon=7.4814&exclude=hourly,minutely&appid={api_key}&units=metric"
    response = requests.get(url)
    data = response.json()
    return data

# Get weather data
data = get_weather_data()

# Convert weather data to a Pandas DataFrame
df = pd.DataFrame(data["daily"])

# Format the date column
df["dt"] = pd.to_datetime(df["dt"], unit="s")
df["date"] = df["dt"].dt.date
df = df.set_index("date")

# Create a line chart using Plotly Express
chart = px.line(df, x=df.index, y="temp.day", title="Dortmund Weather Forecast")
chart.update_xaxes(title="Date")
chart.update_yaxes(title="Temperature (°C)")

# Display the chart in the Streamlit app
st.plotly_chart(chart) In general, that's not too bad. ChatGPT also gave some instructions on how to get the API key. Unfortunately, it didn't tell me I also have to install , but as I asked for a Streamlit app, it's OK to assume I know how to install libraries. plotly Nevertheless, there are two issues: The temperature value is not parsed correctly. It is saved as a dictionary and accessing it with will throw an error. A quick and easy fix would be to replace this with . I assume that's a good example of how the model has learned a lot of different ways how to handle the reply of the API but isn't successful in drawing a connection between parsing the data and accessing it for the chart again. y="temp.day" y=df['temp'].str['day'] The app only works if there is a valid API key and throws an error if there is none. The missing error handling is unfortunate as it displays error messages in the front end. However, it could quickly be fixed by either catching the error and displaying a more user-friendly error message (preferred) or implementing the API call so that the error is not displayed in the UI. Apart from these issues, the code looks fine and runs after fixing the first issue. Also, while the number of comments might be too ambitious in general, it's helpful to understand the generated code. Writing tests with ChatGPT Next, I asked ChatGPT to write a unit test for the application. import unittest
from app import get_weather_data

class TestWeatherApp(unittest.TestCase):

    def test_weather_data(self):
        # Call the function to get weather data
        data = get_weather_data()

        # Check that the data contains the expected keys
        self.assertIn("current", data)
        self.assertIn("hourly", data)
        self.assertIn("daily", data)

        # Check that the daily data contains the expected keys
        daily_data = data["daily"][0]
        self.assertIn("dt", daily_data)
        self.assertIn("temp", daily_data)
        self.assertIn("feels_like", daily_data)
        self.assertIn("pressure", daily_data)
        self.assertIn("humidity", daily_data)
        self.assertIn("dew_point", daily_data)
        self.assertIn("wind_speed", daily_data)
        self.assertIn("wind_deg", daily_data)
        self.assertIn("weather", daily_data)
        self.assertIn("clouds", daily_data)
        self.assertIn("pop", daily_data)
        self.assertIn("uvi", daily_data) This one is interesting. First of all, the test calls get_weather_data(), but the API key is defined outside the function. Therefore, it will run without an API key and will always fail. So the test is useless. Apart from that, this is not a unit test but an integration test. It tests if the (external) API returns the correct data. To implement a unit test, expected data should be mocked, so I asked ChatGPT to fix this. This is not a unit test but an integration test. Can you fix this? import unittest
from unittest.mock import patch
from app import get_weather_data

class TestWeatherApp(unittest.TestCase):

    @patch('requests.get')
    def test_get_weather_data(self, mock_get):
        # Set up test data
        expected_data = {'daily': [
            {'dt': 1645808400,
            'temp': {'day': 5.5, 'min': 2.15, 'max': 7.9, 'night': 3.4, 'eve': 7.9, 'morn': 2.15},
            'feels_like': {'day': 0.3, 'night': -3.04, 'eve': 4.06, 'morn': -4.01},
            'pressure': 1028, 'humidity': 51, 'dew_point': -2.57, 'wind_speed': 2.81, 'wind_deg': 194,
            'weather': [{'id': 801, 'main': 'Clouds', 'description': 'few clouds', 'icon': '02d'}],
            'clouds': 16, 'pop': 0, 'uvi': 2.21}],
            'timezone_offset': 3600}

        # Set up mock response from requests.get
        mock_response = type(
                'MockResponse',
                (object,),
                {'json': lambda self: expected_data}
            )
        mock_get.return_value = mock_response

        # Call the function to get weather data
        data = get_weather_data()

        # Check that the function returns the expected data
        self.assertEqual(data, expected_data) This looks much better, but still, I am missing testing for potential error cases, so I asked again: Shouldn't the unittest also check if the app crashes in case something unusual happens. For example, if the API is down? ChatGPT added this part to the code generated earlier: @patch('requests.get')
    def test_get_weather_data_error(self, mock_get):
        # Set up mock response from requests.get to raise an exception
        mock_get.side_effect = Exception("Connection error")

        # Call the function to get weather data
        with self.assertRaises(Exception):
            data = get_weather_data() This addition looks reasonable. However, as the original app does not handle any error cases, this test doesn't make much sense. Testing if the app doesn't crash when we know it will crash leads nowhere, so I stopped asking. Overall, the results presented by ChatGPT were OK. They certainly help with prototyping or giving a template to start from. However, the code still needs to be made production-ready. ChatGPT would certainly help with that, but improving the code step by step by asking the right questions is not an efficient way of working. Outlook Last year, Deepmind's AlphaCode took part in competitive programming contests and The code and some comments by a human competitor can be found . This has been quite a breakthrough as it was the first time an AI coding model proved problem-solving skills. It will be interesting to see how these models improve and find their way into software development. performed like a median human competitor. here Also published . here

The Current Reality and Future Possibilities of AI-Powered Coding

Are you aware of how the were carried out when there was no internet or computers? Back then, stock exchanges had active trading floors filled with brokers and traders. To make a trade or a purchase, they had to shout or use hand signals to alert others about their buy or sell orders. It looked a whole lot like an auction at a fish market today. buying and selling of stocks But then came computers and the internet to change the game completely. That not only replaced the archaic trading system on the floors of exchanges but also made it possible for individual investors to participate in markets on an equal footing. But that wasn't the last market-altering shift that would happen. The Algorithmic or Automated Trading Era Next came the era - where computers are programmed to trade independently when specific criteria are met. This made it possible to execute complex trading strategies with pinpoint precision. Investors and brokers gained the ability to plot their strategies many moves in advance. And they could also protect themselves from downside risk with automated stop-loss orders. algorithmic or automated trading The new trading systems also made a variety of possible. They took automated trading to the next level by allowing computers to trade based on a variety of price signals analyzed in real-time. And since the computers can execute a near-limitless number of trades in an instant, high-frequency trading to exploit every minute price movement became the norm. useful algorithmic trading tools Algorithmic trading strategies also came with another advantage. Traders could use software like and platforms like to backtest their strategies against historical market data. That meant they would know their strategy's odds of success before committing real money to it. It marked the final shift from intuition-based trading to data-backed analytical trading. MetaTrader 5 TradingView Enter Artificial Intelligence But algorithmic trading wasn't perfect. Using algorithmic trading, we can tell a computer to do A when B occurs. For example, an order to buy a stock (A) when RSI<30 (B). When condition B (RSI<30) is met, the computer performs action A (buys the stock). That's automated trading. Humans program and specify conditions in advance and the computer does everything else. But preprogrammed strategies can fall apart when confronted with contradictory or unexpected data. It's a vulnerability that has already spurred in the recent past. numerous mass-selloffs Artificial intelligence (AI), however, goes a step further. AI trading solutions can train on multiple types of data to understand how and why markets move as they do. Applying AI to the task of stock trading can allow computers to figure out those A and B conditions themselves and get results with minimum risk. And they should also have the ability to shift strategies on the fly with no further human intervention. Human Intelligence vs. Artificial Intelligence The great thing about AI systems is that they can process gigantic amounts of information in mere seconds, far exceeding the ability of human traders. The fintech app , for example, built proprietary AI-driven algorithms to instantly analyze information from a variety of data sources. It considers historical company and stock data, international markets, news announcements and social sentiment, financial reports, and even stocks' compatibility with investors' existing portfolios. Front The result is to quickly show investors the potential risk for any investment, so they can make more informed decisions in a fraction of the time. AI systems consider all the technical, fundamental, sentimental, and lots of other micro and macro factors to make the correct evaluation in real-time. They bring some of the art of the old-school intuition-based trader together with the power of data-backed analysis. And stock trading AI systems are improving fast. This past June, researchers at the University of Cagliari reported the results of a project that used a convolutional neural network (CNN) to power a buy and hold stock trading strategy. The CNN trained on historical S&P 500 data and could predict the live market with 50% accuracy. And with some minor modifications, . it did better than break even Is AI a Game Changer? While it's still too early to declare AI the next big thing in stock trading, all of the early signs are pointing in that direction. Already, basic AI systems can do a better-than-average job at predicting market movements. And they don't suffer from the tendency to , as human traders do. They approach every situation with cool rationality – which means they can exploit market movements that run counter to the underlying data. trade based on emotions AI can also protect traders from mistakes and work to stabilize markets, unlike algorithmic trading. A wide range of tasks (automation spectrum) can be performed without human intervention through AI that was impossible before. fat-finger trading With AI, can evaluate millions of data points and carry out trades at the most favorable price points, and analysts can make market predictions with increased accuracy. And AI can also help large trading firms to mitigate risk while aiming at the highest possible gains. Robo-advisors However, to perform such functions, a large amount of data is required. An AI model can only provide superior predictions when the primary dataset it learns from is large and diverse. And developers must ensure to utilize training methods that don't leave the AI with blind spots or inherent biases. Only then can an AI-based trading system have a solid foundation upon which to build. Pitfalls of AI in Trading With that said, some factors could prevent AI from coming to dominate financial markets. As with any other type of technical innovation, there are bound to be missteps along the way. And considering the speed of today's markets and their growing dependence on automation, even a slight hiccup could cause havoc. And there's also the possibility of AI being used for purposeful market manipulation. Back in 2010, a rogue trader used an algorithm to try and sway markets to their advantage – . With AI in the mix, such schemes might grow ever-more sophisticated, depending on what kind of fail-safe mechanisms evolve to constrain automated traders. with disastrous results And there's still the matter of AI's immaturity as a technology. Even now, as impressive as AI trading systems are, they can't fully replicate human intuition. That could leave AI systems open to external manipulation. For example, suppose an AI system tracks an influencer, Mr. X, using sentiment analysis. It would be up to its training data to inform a decision on how to weigh Mr. X's opinions. The AI can't yet factor in real-time contextual data. However, a seasoned human trader might have known that Mr. X's followers, given his history, would discount his opinion and act accordingly – producing a contradictory result. And if an individual influencer carried enough weight with an AI, they might decide to try and influence that AI's decision-making for their own benefit. The possibility of such outside manipulation – however remote – will remain a stumbling block for AI trading systems. That is until they evolve further and can make more intuitive decisions on their own. AI Trading Offers an Edge Over Algorithm Trading What is clear, however, is that AI-powered trading holds several advantages over its algorithm-only predecessors. First, there's the fact that an algorithm-driven system is always limited by the skills of the person using it. Because they cannot conduct independent analysis, they won't always deliver profitable results. An AI-powered system, by contrast, can make decisions based on rich data in real-time. AI can process massive amounts of data far faster than a human trader ever could, which increases the chances that it will make sound and reasoned decisions. And it can make those decisions fast enough to exploit even the tiniest market movements, or to counteract unexpected market swings by remaining flexible. Managing Risks All of these factors mean that AI is already one of the best tools available to forecast which stocks to invest in. The risk-management benefits of current-generation AI tools alone make it more than worth using. And for that reason, it's no surprise that AI is already in use by the stock markets themselves for that purpose. NASDAQ, for example, adopted an AI-powered anti-fraud system at the end of 2019. It's purpose-built to that caused the flash crash referenced earlier. And because the system can learn to spot other types of malicious activity, it may come to act as a check on some of the pitfalls of AI-powered trading identified here. detect and defeat the exact type of market manipulation The Bottom Line The bottom line is that AI has already revolutionized the stock market. It may not be as visible as the technologies it's displacing, but its influence is undeniable. And this is just the beginning. Moving forward, there's an excellent chance that AI will continue to evolve new feature sets to aid investors, brokers, and market regulators. But even at this early stage, AI-powered trading tools can reduce the time, in-depth knowledge, and intuition necessary to build a strong portfolio for the long term. We haven't reached the point where human traders become irrelevant, but today's AI has closed the gap between casual investors and professional traders considerably. And given how fast that has happened – it won't be long before AI becomes the new foundation of the stock markets of the future. Photos licensed via contributor's Adobe Stock account, by metamorworks, peshkov, AndSus.

The Evolution and Future of AI in the Stock Market

This article about AI in fintech services is originally written for Django Stars blog. Artificial Intelligence is not a new kid on the block, but rather an integral part of a growing number of industries. Forward-thinking executive managers and business owners actively explore new AI use in finance and other areas to get a competitive edge on the market. First used mostly in computer science, AI now has advanced to the most important areas of our life, e.g.: Transportation Education Health care . Financial services The rise of AI in the shows how fast it’s gaining traction, even in traditionally conservative areas, and how quickly it’s changing the business landscape. Here are just some of the most popular examples of AI in finance. financial industry 1. AI and Credit Decisions Artificial Intelligence provides a faster, more accurate assessment of a potential borrower, at less cost, and accounts for a wider variety of factors, which leads to a better-informed, data-backed decision. Credit scoring provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems. It helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history. Objectivity is another benefit of the AI-powered mechanism. Unlike a human being, a machine is not likely to be biased. Digital banks, loan-issuing apps and use machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalized options. online mortgage platforms Automobile lending companies in the U.S. have reported success with AI for their needs as well. For example, this report that bringing AI on board cut losses by 23% annually. shows 2. AI and Risk Management It’s difficult to overestimate the impact of AI in financial services when it comes to risk management. Enormous processing power allows vast amounts of data to be handled in a short time, and cognitive computing helps to manage both structured and unstructured data, a task that would take far too much time for a human to do. Algorithms analyze the history of risk cases and identify early signs of potential future issues. Artificial intelligence in fintech is a powerful ally when it comes to analyzing real-time activities in any given market or environment; the accurate predictions and detailed forecasts it provides are based on multiple variables and vital to business planning. A US leasing company, Crest Financial, employed artificial intelligence on the Amazon Web Services platform and immediately saw a significant improvement in risk analysis, without the deployment delays associated with traditional data science methods. You May Also Like: _The thing I like about finance is that this industry is as old as time - and yet, few people dare enter it. Luckily…_djangostars.com What You Need To Consider Before Building a Fintech Product - Django Stars Blog _One of the most famous travellers of all times, Christopher Columbus, made only 4 journeys during all his life. One of…_djangostars.com Machine Learning and Data Analytics in the Travel Industry 3. AI and Fraud Prevention For a number of years now, artificial intelligence has been very successful in battling financial fraud — and the future is looking brighter every year, as machine learning is catching up with the criminals. AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern. Banks also employ artificial intelligence to reveal and prevent another infamous type of financial crime: money laundering. Machines recognize suspicious activity and help to cut the costs of investigating the alleged money-laundering schemes. One reported a 20% reduction in the investigative workload. Case study Aggregators like (which works with financial giants like CITI, Goldman Sachs and American Express) take pride in their fraud-detection capabilities. Its complex algorithms can analyze interactions under different conditions and variables and build multiple unique patterns that are updated in real time. Plaid that connects a bank with the client’s app to ensure secure financial transactions. Plaid works as a widget 4. AI and Trading Data-driven investments have been rising steadily over the last 5 years and . It’s also called algorithmic, quantitative or high-frequency trading. closed in on a trillion dollars in 2018 This kind of trading has been expanding rapidly across the world’s stock markets, and for good reason: artificial intelligence offers multiple significant benefits. Intelligent Trading Systems monitor both structured (databases, spreadsheets, etc.) and unstructured (social media, news, etc.) data in a fraction of the time it would take for people to process it. And nowhere is the saying “time is money” truer than in trading: faster processing means faster decisions, which in turn mean faster transactions. offer more accurate predictions for stock performance due to the fact that algorithms can test trading systems based on past data and bring the validation process to a whole new level before pushing it live. Online investment platforms AI puts together recommendations for the strongest portfolios depending on a specific investor’s short- and long-term goals; multiple financial institutions also trust AI to manage their entire portfolios. The business news outlet, Bloomberg, recently launched , a price-forecasting application for investors powered by AI. It combines real-time market data provided by Bloomberg with an advanced learning engine to identify patterns in price movements for high-accuracy market predictions. Alpaca Forecast AI Prediction Matrix 5. AI and Personalized Banking Artificial intelligence truly shines when it comes to exploring new ways to provide additional benefits and comfort to individual users. In the banking sector, AI powers the smart chatbots that provide clients with comprehensive self-help solutions while reducing the call-centers’ workload. Voice-controlled virtual assistants powered by smart tech like Amazon’s Alexa are also gaining traction fast, which is no surprise: boasting a self-education feature, they get smarter every day, so you should expect tremendous improvements here. Both tools can check balances, schedule payments, look up account activity and more. A number of apps offer personalized financial advice and help individuals achieve their financial goals. These intelligent systems track income, essential recurring expenses, and spending habits and come up with an optimized plan and financial tips. The biggest US banks, such as Wells Fargo, Bank of America and Chase, have launched mobile banking apps that provide clients with reminders to pay bills, plan their expenses and interact with their bank in an easier and more streamlined way, from getting information to completing transactions. 6. AI and Process Automation Forward-thinking industry leaders look to robotic process automation when they want to cut operational costs and boost productivity. Intelligent character recognition makes it possible to automate a variety of mundane, time-consuming tasks that used to take thousands of work hours and inflate payrolls. Artificial intelligence-enabled software verifies data and generates reports according to the given parameters, reviews documents, and extracts information from forms (applications, agreements, etc.). Employing robotic process automation for high-frequency repetitive tasks eliminates the room for human error and allows a financial institution to refocus workforce efforts on processes that require human involvement. Ernst & Young has a 50%-70% cost reduction for these kinds of tasks, and Forbes it a “Gateway Drug To Digital Transformation”. reported calls A leading financial firm, JP Morgan Chase, has been successfully leveraging Robotic Process Automation (RPA) for a while now to perform tasks such as extracting data, comply with Know Your Customer regulations, and capture documents. RPA is one of ‘ ‘ JP Morgan Chase uses to enhance the cash management process. five emerging technologies What to Expect in The Future From AI in the Financial Industry Predictions for the soon-to-come AI applications in financial services is a hot topic these days but one thing is for sure: AI is rapidly reshaping the business landscape of the financial industry. There are high hopes for increased transactional and account security, especially as the adoption of blockchains and cryptocurrency expands. In turn, this might drastically reduce or eliminate transaction fees due to the lack of an intermediary. All kinds of digital assistants and apps will continue to perfect themselves thanks to cognitive computing. This will make managing personal finances exponentially easier, since the smart machines will be able to plan and execute short- and long-term tasks, from paying bills to preparing tax filings. We can also expect to see better customer care that uses sophisticated self-help VR systems, as natural-language processing advances and learns more from the expanding data pool of past experience. A new level of transparency will stem from more comprehensive and accurate know-your-client reporting and more thorough due-diligence checks, which now would be taking too many human work hours. Conclusion As we can see, the benefits of AI in financial services are multiple and hard to ignore. According to , 65% of senior financial management expects positive changes from the use of AI in financial services. Forbes This said, as of late 2018, only a third of companies have taken steps to implement artificial intelligence into their company processes. Many still err on the side of caution, fearing the time and expense such an undertaking will require — , and there will be challenges to implementing AI in financial services. However, one can’t shy away forever from technological progress and not facing it now may cost more in the long run.

AI to Transform The Financial Services: Six Examples

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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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