Information Theory and Ensemble Models

Traditional distance-based metrics like MSE and RMSE increasingly fail to differentiate between optimized forecasting models, making ensemble weighting difficult. This piece proposes using spectral entropy from information theory as an alternative metric for ensemble model weighting in time-series forecasting. Applied to inflation data (CPI, PPI, savings rate, business inventories), the author demonstrates that entropy-based inference can separate model performance where distance metrics cannot. A rough entropy inference ensemble scheme is outlined and compared against a distance-based ensemble, with results showing comparable accuracy but room for improvement. The approach is framed as nascent and open to extension, with entropy optimization suggested as a natural next step.

Nguồn: https://towardsdatascience.com/information-theory-and-ensemble-models-ded31db10d8. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.

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Why Forecasting Infectious Diseases Is Harder Than Predicting Sales (And What Data Scientists Can Learn From It)

Forecasting infectious disease activity differs fundamentally from typical time-series problems like retail sales. Key lessons include: model performance varies significantly by country due to differing epidemiology; strongly seasonal diseases are paradoxically harder to forecast because peak timing and magnitude must all be correct; standard metrics like RMSE can mask critical errors in public health contexts; domain-informed feature engineering often outperforms algorithm-switching; and anomaly detection complements forecasting to catch unexpected divergences. The broader takeaway for data scientists is to shift focus from 'which model' to questions about validation strategy, meaningful metrics, adaptive pipelines, and knowing when a model is wrong.

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