arXiv:2610.33822v1 Announce Type: cross Abstract: A central challenge in multivariate forecasting is deciding what to share across channels and where to specialize. Mixture-of-experts models offer fl...
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
The paper introduces Internal Dual-Wiener routing (Internal‑DW), a backward‑only method that weight‑balances internal gradient routes in autoregressive forecasting. By estimating bounded Wiener gains for identity and nonlinear paths, it suppresses unpredictable noise while preserving predictable learning signals, reducing forecast error by 5.2%–13.8% on four weak‑drive testbeds compared to full BPTT and outperforming gradient clipping, Jacobian regularization, and truncated BPTT in most cases. The approach shows that long‑horizon supervision can be effective without trusting every backward gradient equally.
arXiv:2608. 01857v1 Announce Type: new Abstract: The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as risk management and financial forecasting.
Irregular multivariate time series forecasting (IMTSF) requires capturing temporal dynamics and cross-variable dependencies from sparse, asynchronous observations. Existing methods often rely on learn...
arXiv:2608. 05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics.
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.
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.
arXiv:2601. 22631v2 Announce Type: replace-cross Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data.
The paper introduces Generalized Gibbs Ensemble Weighting (GGEW), a probabilistic framework that assigns weights to forecasting models using a Gibbs-style exponential transformation of normalized predictive loss. GGEW extends basic weighting through numerical stabilization, diversity-aware score corrections, and online hyperparameter adaptation, yielding variants such as Stable Gibbs weighting, Directional Gibbs-NCL, and Symmetric Gibbs-NCL. The authors evaluate GGEW on M4 competition submissions and real-world datasets (Monash Traffic, Electricity, Solar), finding that Gibbs-style adaptive weighting is competitive across various settings, though performance varies by dataset, horizon, and deployment protocol.
The paper introduces a regime‑diagnosis framework for industrial time‑series forecasting, highlighting that canonical loss functions embed fixed statistical priors that are violated in real‑world demand regimes such as zero‑inflation, skewness, and high variability. It proposes the Regime‑wise Relative Bias Vector (RBV) as a metric‑agnostic diagnostic that decomposes bias into an intrinsic floor and an excess attributable to training. A large‑scale study across 13 loss objectives and 60,000+ series demonstrates that regime‑aware diagnosis distinguishes optimization‑from‑bias failures and that regime‑aware training can eliminate pooling‑induced bias that mere capacity scaling cannot.
CORE-STACK+ is a new meta‑learning framework for deep stacked generalization that tackles two key problems in heterogeneous vision ensembles: prediction‑space multicollinearity and calibration collapse. It introduces a four‑step preconditioning pipeline—kernelized redundancy filtering, a lightweight differentiable meta‑feature gate, a spectrum‑adaptive ridge penalty, and a Laplace‑approximate Bayesian blender—to jointly improve conditioning and calibration. Across six vision benchmarks, CORE‑STACK+ boosts accuracy, reduces model count and inference cost, and significantly lowers expected calibration error compared to existing methods.