AltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2602.11533v1
• Date: Thu, 12 Feb 2026 03:45:00 GMT
• Title: AltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting
• Authors: Zhihang Yuan, Zhiyuan Liu, Mahesh K. Marina,
• Abstract summary: We propose ALTTS, a dual-path framework that explicitly decouples autoregression and cross-relation modeling.<n>We show that ALTTS consistently outperforms prior methods, with the most pronounced improvements on long-horizon forecasting.
• Score: 27.971282358985604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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