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Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls by@vkirilin
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Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls

by Vladimir Kirilin13mApril 3rd, 2024
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I focus on methods to handle missing data in financial time series. Using some some example data I show that LOCF is usually a decent go-to method compared to dropping and imputation but has its faults - i.e. can create artificial undesirable jumps in data. However, alternatives like interpolation have their own problems especially in context of live prediction/forecasting.
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Vladimir Kirilin

Vladimir Kirilin

@vkirilin

Quant @ Five Rings Capital

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Opinion piece / Thought Leadership

Opinion piece / Thought Leadership

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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Vladimir Kirilin@vkirilin
Quant @ Five Rings Capital

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