The paper investigates whether entire Transformer blocks can be skipped during inference in two time‑series foundation models, Chronos‑Bolt and TimesFM 2.5. By applying block‑level masks selected on calibration windows, the authors show that moderate sparsity (e.g., 7/11 blocks for Chronos‑Bolt and 16/20 for TimesFM) preserves forecasting quality while reducing latency by roughly 11–13%. However, more aggressive skipping degrades performance on certain datasets, and dynamic, input‑dependent routing remains unproven across architectures.
arXiv:2606. 04074v1 Announce Type: cross Abstract: Adaptive patching is a recent and compelling proposal for time-series Transformers: allocate finer patches where the sequence looks locally informative.
arXiv:2608.20024v2 Announce Type: replace Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Forecasting models trained on historical da...
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: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.
District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes.
arXiv:2609.13640v1 Announce Type: new Abstract: Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the fin...
arXiv:2607. 04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost.
arXiv:2608. 20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling.
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:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.
arXiv:2601.01480v3 Announce Type: replace-cross Abstract: Traffic forecasting systems rely on fixed sensor networks that frequently exhibit contiguous blackouts. Such outages are usually treated as i...
arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.