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Data Leakage, Lookahead Bias, and Causality in Time Series Analytics
The article examines three critical challenges in time series analytics: data leakage, lookahead bias, and causality violations, demonstrating their impact through practical examples with natural gas
price data. It provides comprehensive solutions and best practices for preventing these issues, including proper data splitting, feature engineering, and causality validation techniques.

Reasons to Read -- Learn:

  • how to identify and prevent data leakage in time series models, with practical code examples showing how improper feature engineering can lead to MAPE differences of 6.07% in model performance
  • how to implement proper time series validation techniques, demonstrated through concrete Python implementations that show how lookahead bias can artificially improve model performance by 18.22 percentage points
  • complexities of causality in time series analysis, including how to use tools like Granger Causality tests to validate relationships between variables, illustrated with real-world examples using natural gas prices
  • publisher: @kylejones_47003
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