ML 36: Shuffling a Time Series Is Safe Until the Errors Remember
Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and drafted the text. The topic, the constraints, the data choices and the final review are the author’s. Seven simulated time-series settings, twenty series each, cross-validated three ways on 1,000 steps and compared with the error on the next 250. On a stationary autoregression with its lags as features, shuffled five-fold cross-validation was within 3% of the future error for linear regression, nearest neighbours and a forest....