Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting
Forecasting multiple time-series with high-dimensional covariates presents a core challenge: unifying common temporal patterns while retaining meaningful series-specific information. We introduce Hopformer (Homogeneity-Pursuit Transformer), a two-stage framework that addresses this challenge. In the first stage, we perform a Sparsity Pattern Aggregation (SPA) scheme extracting a common low-variance trend that incorporates the covariates. This acts as a homogenization layer. In the second stage, a LoRA-fine-tuned Transformer models the remaining complex dependencies in the residual. Our method is theoretically grounded. We prove that SPA achieves a near-optimal bias-variance trade-off via ...
| Authors | Wan Zhang, Qinjie Lin, Chan Lee et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 July 2026 |
| Last updated at source | 13 October 2026 |
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