AdaCast introduces a conditional parameter generation framework that tailors time‑series forecasting models to each input by producing low‑rank updates for a frozen pretrained foundation model. Unlike static adaptation methods that apply a single set of updates to all inputs, AdaCast generates input‑specific updates during both training and inference. Experiments on six public benchmarks show that AdaCast consistently outperforms static baselines in in‑domain forecasting and improves zero‑shot generalization to unseen datasets across domains.
By Darahaas Nallagatla, Darryl Cherian Jacob, Pan He
The paper introduces Chameleon, a channel‑dependent state space model for multivariate time series forecasting that allows data‑dependent, fine‑grained interactions across variables while maintaining linear scaling with the number of variables. By integrating selective state space models with a Kalman filter and adapting GatedDeltaNet as the backbone, Chameleon improves generalization and achieves lower MSE and MAE on strongly dependent ODE and PEMS datasets compared to both channel‑independent and prior channel‑dependent methods. Across 28 benchmark settings, it outperforms baselines in the majority of cases and demonstrates competitive training‑time and memory efficiency on Traffic and ETT datasets.
By Yu-Cheng Wu, Fan-Keng Sun, Li-Chun Lu, Duane S. Boning
The paper introduces series‑wise cross‑validation (CV) for global time‑series forecasting models, contrasting it with the traditional time‑wise CV that partitions data along the time axis. It formalizes several series‑wise CV schemes—K‑fold, bootstrapping, and Monte Carlo—and evaluates them on 11 datasets with 10 neural network architectures across five metrics, including performance estimation, overoptimism, model selection accuracy, regret, and downstream forecasting performance. The study finds that series‑wise CV improves downstream forecasting accuracy by better guiding model selection for generalization across both time and series, while time‑wise CV slightly outperforms it for estimating out‑of‑sample error.
By Vitor Cerqueira, Mariana Oliveira, Luis Roque
The paper investigates how post‑generation assembly of fixed‑length synthetic windows affects downstream forecasting performance. By varying overlap rates and window‑weighting schemes, the authors evaluate boundary continuity, statistical and temporal fidelity, and TSTR utility across four public datasets and five forecasting models. Results show that optimal assembly depends on the forecaster, with increased overlap improving continuity but not always reducing forecasting error, and provide assembly grids and TRTR references to quantify these effects.
By Guoxiong Long, Huizhen Huang, Qikun Cai, Tao Huang, Chen Hou
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
By Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei
arXiv:2610.03494v1 Announce Type: new
Abstract: Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidenc...
By Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme