DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

TL;DR

DualWeaver is a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series, which outperforms state-of-the-art multivariate forecasters in both accuracy and stability.

Abstract

Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasting remains challenging. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series. Generated by a shared auxiliary feature-fusion module that captures cross-variable dependencies, these surrogates are mapped to TSFM-compatible series via the forecasting objective. The symmetric structure enables parameter-free reconstruction of final predictions directly from the surrogates, without additional parametric decoding. A theoretically grounded regularization term is further introduced to enhance robustness against adaptation collapse. Extensive experiments on diverse real-world datasets show that DualWeaver outperforms state-of-the-art multivariate forecasters in both accuracy and stability. We release the code at https://github.com/li-jinpeng/DualWeaver.

Figures (10)

• Figure 1: The paradigms adapting Uni-TSFM for multivariate forecasting. (a) Channel-independent: each variable is processed separately by Uni-TSFMs, ignoring cross-variable dependencies. (b) Encoder-Decoder: an encoder maps original variables to latent representations, whose predictions are then decoded back; the decoding may mislead the adaptation. (c) Dual-tuned Surrogates (ours): a shared feature-fusion module generates dual surrogates that capture dependencies under distinct optimization directions, reducing overfitting and making adaptation more robust.
• Figure 2: The overview of the DualWeaver framework. A shared feature-fusion module first extracts cross-variable dependencies to generate a pair of dual-tuned surrogates ($\mathbf{S}_\alpha$, $\mathbf{S}_\beta$). These surrogates are independently processed by a frozen Uni-TSFM, after which the final multivariate predictions are derived via non-parametric reconstruction to neutralize the shared fusion part.
• Figure 3: Performance comparison between Sundial (128M) and Chronos-2 (119M). While Chronos-2 benefits from cross-variable interactions in certain scenarios, DualWeaver enables the adapted Sundial to approach or exceed such performance.
• Figure 4: Predictive stability under increasing noise. DualWeaver remains robust to additional noise, outperforming baselines that degrade progressively. Done with forecasting horizon at 96.
• Figure 5: Predictability enhancement in the surrogate space. DualWeaver transforms complex multivariate signals into predictable surrogates, yielding an average reduction of 85.44% in MSE and 61.58% in MAE compared to the original input space.