Series-wise Cross-validation for Global Time Series Forecasting
Read the original on arXiv Machine Learning →The paper introduces series‑wise cross‑validation (CV) for global time‑series forecasting models, contrasting it with the traditional time‑wise CV that partitions data along the time axis. It formalizes several series‑wise CV schemes—K‑fold, bootstrapping, and Monte Carlo—and evaluates them on 11 datasets with 10 neural network architectures across five metrics, including performance estimation, overoptimism, model selection accuracy, regret, and downstream forecasting performance. The study finds that series‑wise CV improves downstream forecasting accuracy by better guiding model selection for generalization across both time and series, while time‑wise CV slightly outperforms it for estimating out‑of‑sample error.
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