A 3.5-hour look into the future: how a model deceived itself for a month and a half — and how we caught it

This article explores a classic machine learning problem in time series analysis: data leakage, where a model implicitly gains information from the future. The authors encountered a situation where a prediction algorithm performed excellently on historical data but failed in real-world scenarios. The cause was incorrect resampling of 4-hour candles, which allowed the model to 'look' 3.5 hours into the future. The material details the anatomy of this error, the mathematical approach to conducting a positional test for its detection, and offers methods to prevent such incidents. The authors share a code snippet that helps prevent data leakage during time series processing, emphasizing the importance of rigorous data pipeline validation to ensure the reliability of ML models in production.