Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

Wan Zhang; Qinjie Lin; Chan Lee; Weijian Li; Han Liu; Kai Zhang

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arXiv · 2607.22299

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

Wan Zhang·Qinjie Lin·Chan Lee·Weijian Li·Han Liu·Kai Zhang

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Abstract

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 an oracle inequality. We also provide generalization bounds for the second stage under dependent time series data. Hopformer sets a new state of the art, improving MASE by an average of 6.56% across synthetic and real-world forecasting benchmarks.

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BibTeX RIS

Wan Zhang, Qinjie Lin, Chan Lee, Weijian Li, Han Liu, Kai Zhang. 2026-07-24. Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting. https://arxiv.org/abs/2607.22299

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