Dealing with Missing Data in Financial Time Series - Recipes and Pitfalls
2024-4-3 23:3:8 Author: hackernoon.com(查看原文) 阅读量:1 收藏

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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 HackerNoon profile picture

Vladimir Kirilin

Vladimir Kirilin

@hackerclrk2ky7l00003j6qwtumiz7c

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 HackerNoon profile picture

Quant @ Five Rings Capital

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