arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.
The paper introduces a simple, model‑agnostic time‑domain augmentation called Sliding‑Window Reordering with Overlap Averaging. It transforms the joint input‑target sequence into overlapping windows, randomly reorders a fraction of them based on a variance criterion, and reconstructs the sequence by averaging overlaps to generate synthetic samples with controlled variation and minimal temporal distortion. Experiments show strong performance gains across nine long‑term forecasting benchmarks and four short‑term traffic benchmarks, with detailed ablations and diagnostics highlighting the effectiveness of each design choice.
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.
arXiv:2608. 05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics.
arXiv:2509. 15394v3 Announce Type: replace Abstract: Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal patterns crucial.
arXiv:2511. 09789v3 Announce Type: replace Abstract: Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential.
The paper presents a hybrid neural architecture that blends linear and nonlinear feed‑forward networks for day‑ahead electricity price forecasting. It introduces a partial online learning strategy with warm‑starting and stage‑specific hyperparameters to cut computational time, and employs Bernstein Online Aggregation to combine forecasts. Experiments on six years of major European markets show the method reduces RMSE by 11‑12% and MAE by 14‑17% compared to state‑of‑the‑art benchmarks while lowering computational cost.
arXiv:2405.07359v2 Announce Type: replace Abstract: Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecast...
arXiv:2607. 02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored.
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
arXiv:2606. 07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences.