AliO: Output Alignment Matters in Long-Term Time Series Forecasting proposes a new method, AliO, to improve output alignment in LTSF models by reducing discrepancies in prediction outputs for the same timestamps across both time and frequency domains. The authors introduce a Time Alignment Metric (TAM) to quantify alignment, contrasting with traditional MSE metrics that only measure distance to ground truth. Experiments demonstrate that AliO can increase TAM by up to 58.2% while maintaining or improving forecasting performance by up to 27.5%.
arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared...
The paper critiques the prevalent use of mean squared error (MSE) for evaluating irregular time‑series forecasting, arguing that MSE is biased by timestamp sampling distributions. It introduces the Continuous‑time Squared Error (CSE), an importance‑weighted metric that theoretically offers a tighter asymptotic bound on continuous‑time risk than MSE. A comprehensive benchmark across synthetic, semi‑synthetic, and eight real‑world datasets demonstrates that CSE more accurately recovers continuous‑time risk, revealing limitations of relying solely on MSE.
arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.
Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficiently studied. Existing benchmarks typically use mean squared error (MSE) as the evaluation metric.
arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.
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.
The paper introduces Multivariate Time Series Super-Resolution Forecasting (MTS‑SRF), a method that predicts fine‑grained future values from coarse historical data. It proposes the TimeMagnifier framework, which includes Fourier Anchor to handle data sparsity, De‑noise Only to refine predictions along the resolution axis, and banded DTW Alignment to correct cross‑resolution misalignment. On a new benchmark of ten datasets across five domains, TimeMagnifier outperforms 15 baselines, reducing mean‑squared error by an average of 12.3% and improving existing forecasters by 5.1% to 9.0% when its components are used as plug‑ins.
arXiv:2608. 10433v4 Announce Type: replace Abstract: Time-series forecasters increasingly accompany numerical predictions with explicit temporal reports, such as delays or selected history, but a correct report need not describe the information actually used by the forecast.
arXiv:2606. 26549v1 Announce Type: new Abstract: Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction.
arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster...
arXiv:2608. 06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models.
NeST is a framework that adapts large language models (LLMs) for continuous time‑series forecasting by creating neighborhood‑aware text prototypes and aligning them with temporal representations through a nearest‑neighbor contrastive objective. It retrieves the most relevant prototypes and uses them to conditionally modulate time‑series features, enabling more effective integration of textual and temporal information. Experiments show that NeST outperforms state‑of‑the‑art methods on eight benchmarks, reduces MSE by 1.2% for long‑term forecasting, improves zero‑shot forecasting by 4.9%, and boosts R² by 3.3% on a real‑world photovoltaic power forecasting task.