AltTS: 一种具有交替优化的双路径框架用于多变量时间序列预测
AltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting
AI总结：
AltTS 提出一种双路径框架，通过交替优化解耦自回归和跨关系建模，提升多变量时间序列预测的长期内预测性能。
摘要：
多变量时间序列预测涉及两个质上不同的因素：(i) 系列内稳定的自回归（AR）动态，和 (ii) 间歇性的跨维度交互，这些交互在长期内可能会变得虚假。我们认为，拟合一个模型来捕捉这两种效应会产生优化冲突：跨维度建模所需的高方差更新会破坏支持自回归的梯度，导致训练脆弱和长期内预测精度下降。为了解决这个问题，我们提出了 ALTTS，一种双路径框架，明确地将自回归和跨关系（CR）建模解耦。在 ALTTS 中，AR 路径使用线性预测器，而 CR 路径使用配备跨关系自注意力（CRSA）的 Transformer；两个分支通过交替优化协调，以隔离梯度噪声并减少跨块干扰。在多个基准上的广泛实验表明，ALTTS 一致优于先前的方法，最显著的改进出现在长期内预测上。总体而言，我们的结果表明，精心设计的优化策略，而不是更加复杂的架构，可能是多变量时间序列预测进步的关键驱动力。
英文摘要：
Multivariate time series forecasting involves two qualitatively distinct factors: (i) stable within-series autoregressive (AR) dynamics, and (ii) intermittent cross-dimension interactions that can become spurious over long horizons. We argue that fitting a single model to capture both effects creates an optimization conflict: the high-variance updates needed for cross-dimension modeling can corrupt the gradients that support autoregression, resulting in brittle training and degraded long-horizon accuracy. To address this, we propose ALTTS, a dual-path framework that explicitly decouples autoregression and cross-relation (CR) modeling. In ALTTS, the AR path is instantiated with a linear predictor, while the CR path uses a Transformer equipped with Cross-Relation Self-Attention (CRSA); the two branches are coordinated via alternating optimization to isolate gradient noise and reduce cross-block interference. Extensive experiments on multiple benchmarks show that ALTTS consistently outperforms prior methods, with the most pronounced improvements on long-horizon forecasting. Overall, our results suggest that carefully designed optimization strategies, rather than ever more complex architectures, can be a key driver of progress in multivariate time series forecasting.
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