arXiv:2610.31869v1 Announce Type: new Abstract: Covariate-aware time series foundation models (TSFMs) accept a future path for a control variable and return a forecast, inviting what-if queries. The...
arXiv:2610.28563v1 Announce Type: new Abstract: Online search activity is widely used as a digital trace of information seeking during epidemics, but its predictive value is often reduced to correlat...
The paper investigates when auxiliary context can genuinely improve multi‑modal time series forecasting. It identifies two necessary dataset‑level conditions: the target must not be dominated by a last‑value shortcut (low autocorrelation) and the context must provide additional information beyond history (non‑zero conditional mutual information). Experiments on a large mixture‑of‑experts model and several fusion mechanisms show that only when both conditions hold does context routing yield a substantial reduction in mean‑squared error; otherwise its contribution collapses to a capacity floor.
arXiv:2608. 03715v1 Announce Type: new Abstract: Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit default swap (CDS) spreads.
arXiv:2610.07080v1 Announce Type: new Abstract: Attention maps are often interpreted as evidence of what a forecasting model uses when making predictions. In our load-forecasting model, a CLS represe...
The paper introduces Error Feedback Calibration (EFC), a test‑time framework that corrects deterministic time‑series forecasts by using revealed errors as causal feedback. EFC builds a Causal Error Diagonal (CED) from overlapping historical forecast errors, applies Cumulative Error Transport (CET) to map this error state to future residuals, and employs Temporal Residual Learning (TRL) to predict remaining residual structure. Experiments across various forecasting backbones and datasets demonstrate that EFC improves performance while limiting over‑correction.
arXiv:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.
RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.
We introduce Timestral, a family of four time series foundation models for probabilistic forecasting, with 5M, 28M, 123M, and 209M parameters. Timestral sets a new state of the art on GIFT-Eval, TIME,...
arXiv:2607. 05450v1 Announce Type: cross Abstract: This paper explores the "Granularity Paradox" in time-series forecasting, wherein finer temporal disaggregation (e.
Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and the convolution that produces the coefficients i...
arXiv:2609.27877v1 Announce Type: cross Abstract: Extreme events (EEs) in chaotic dynamics are rare broad excursions whose forecastability can be altered by dynamical noise. We investigate how noise...