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arXiv Machine Learning By Guilherme Afonso Galindo Padilha, Paulo Salgado Gomes de Mattos Neto, Rafael Menelau Oliveira e Cruz

Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance

Read the original on arXiv Machine Learning →

The paper investigates how preprocessing choices influence time‑series forecasting performance. By evaluating 11 models across 16 reversible preprocessing pipelines on 29,000 M4 series, it shows that optimizing preprocessing can improve accuracy by 27% to 87%, especially for models without built‑in preprocessing. This demonstrates that simpler architectures can rival complex state‑of‑the‑art models when preprocessing is properly handled.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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