Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

arXiv stat.ML5d4 min read

arXiv:2607.22299v1 Announce Type: new 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 c

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Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

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