Comprehensive time series data science skill covering feature engineering, model training, and competition-winning strategies for forecasting and prediction problems.
Initial release of the Time Series Data Science skill, a comprehensive end-to-end guide and toolkit for competitive time series forecasting and prediction. - Covers feature engineering (lags, rolling stats, EWM, target encoding, interactions), model training, and robust validation strategies tailored for time series. - Provides proven best practices and code templates for LightGBM training, ensembling, and horizon-specific modeling. - Includes a full EDA checklist, practical competition workflow diagram, and ready-to-use pipeline commands. - Lists common pitfalls in time series forecasting and actionable solutions. - Designed for integration with related data science and analytics workflows.