aiqbench analysis
This research introduces a novel time-domain augmentation technique that reorders overlapping sliding windows to enhance deep learning models for multivariate forecasting without disrupting temporal coherence. By leveraging variance-based prioritization and overlap averaging, the method achieves significant performance gains across diverse benchmarks while remaining model-agnostic with minimal hyperparameters. The study provides rigorous validation through extensive ablations and probabilistic evaluations, offering a robust tool for improving long-term time series prediction in AI systems.
Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting
arXiv:2604.09067v2 Announce Type: replace Abstract: Augmentation has become a central technique for improving deep forecasting models, but classification-style transformations tend to break the coherence between the look-back window and its continuous future target. We describe a simple procedure that unfolds the joint input-target sequence into overlapping sliding windows, randomly reorders a controlled fraction of them-prioritized by a lightweight variance criterion-and reconstructs the sequence by averaging across the overlaps, producing synthetic samples with controlled variation while limiting temporal distortion. The procedure is model-agnostic, introduces only three interpretable hyperparameters, and achieves strong improvements over a comprehensive set of competing augmentations across nine long-term forecasting benchmarks with five backbone families (TSMixer, DLinear, PatchTST, TiDE, LightTS) and four short-term traffic benchmarks with PatchTST. Component-wise ablations, hyperparameter sensitivity studies, distributional-alignment diagnostics, probabilistic forecasting evaluation, and a transfer experiment to univariate and multivariate time series classification clarify the contribution of each design choice.
Abstract:Augmentation has become a central technique for improving deep forecasting models, but classification-style transformations tend to break the coherence between the look-back window and its continuous future target. We describe a simple procedure that unfolds the joint input-target sequence into overlapping sliding windows, randomly reorders a controlled fraction of them-prioritized by a lightweight variance criterion-and reconstructs the sequence by averaging across the overlaps, producing synthetic samples with controlled variation while limiting temporal distortion. The procedure is model-agnostic, introduces only three interpretable hyperparameters, and achieves strong improvements over a comprehensive set of competing augmentations across nine long-term forecasting benchmarks with five backbone families (TSMixer, DLinear, PatchTST, TiDE, LightTS) and four short-term traffic benchmarks with PatchTST. Component-wise ablations, hyperparameter sensitivity studies, distributional-alignment diagnostics, probabilistic forecasting evaluation, and a transfer experiment to univariate and multivariate time series classification clarify the contribution of each design choice.
Accepted at CIKM 2026 (Full Research Paper, oral presentation). Revised after peer review. This arXiv version includes an extended appendix with additional experimental results, ablations, and classification details
Machine Learning (cs.LG)
arXiv:2604.09067 [cs.LG]
(or arXiv:2604.09067v2 [cs.LG] for this version)
https://doi.org/10.1145/3799682.3840727 DOI(s) linking to related resources
Full text under the publisher's open licence · 187 words
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