The unreasonable difficulty of time series forecasting

Time series forecasting is fundamentally more difficult than traditional machine learning because time series data comes from a single trajectory rather than independent samples, resulting in low signal-to-noise ratios, reduced effective sample sizes due to autocorrelation, and increased vulnerability to distribution shifts. This explains why sophisticated ML models often underperform simple statistical baselines and foundation models on forecasting tasks, creating significant challenges for organizations relying on predictive analytics for business decisions. CIOs should recognize that throwing advanced AI/ML at forecasting problems without understanding these structural limitations is unlikely to yield competitive advantages and may require rethinking data strategy and investment priorities.