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.
arXiv:2505. 15354v3 Announce Type: replace Abstract: Time-series forecasting is a critical task in various business domains, but it remains inherently challenging.
arXiv:2512. 22702v2 Announce Type: replace Abstract: Deep learning models have grown popular in time series applications.
Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far lower cost by tuning preprocessing rather than scaling models.
arXiv:2509. 26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models.
arXiv:2606. 27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy.
arXiv:2603. 15506v2 Announce Type: replace-cross Abstract: We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonalities, obscures real progress by overlooking the performance of efficient classical methods.
arXiv:2510. 04487v5 Announce Type: replace Abstract: While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs).
arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.
The Nixtlaverse is an open‑source Python ecosystem that unifies statistical, machine‑learning, and neural forecasting models by sharing a common long‑format panel data structure and keyed forecast outputs while preserving each model family’s specialized implementation. Using the M5 competition data, the authors demonstrate the framework’s ability to evaluate diverse models in a single rolling‑origin setup, profile runtime and memory usage across thousands of series, and reconcile forecasts from multiple engines, including an external one, over the full M5 hierarchy. The paper highlights the design’s cost, boundary, and utility advantages and notes its growing adoption and permissive licensing.
arXiv:2606. 10798v1 Announce Type: new Abstract: Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series.
arXiv:2606. 27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis.