Graph-patchformer: Patch interaction transformer with adaptive graph learning for multivariate time series forecasting.

Abstract

Multivariate time series (MTS) forecasting plays a pivotal role in the digitalization and intelligent development of modern society, while previous MTS forecasting methods based on deep learning often rely on capturing intra-series dependencies for modeling, neglecting the structural information within MTS and failing to consider inter-series local dynamic dependencies. Although some approaches utilize multi-scale representation learning to capture inter-series dynamic dependencies at different time scales, they still require additional multi-scale feature fusion modules to output the multi-scale representation of final forecasting results. In this paper, we propose a novel deep learning framework called Graph-Patchformer, which leverages structural encodings to reflect the structural information within MTS while capturing intra-series dependencies and inter-series local dynamic dependencies using the Patch Interaction Blocks we proposed. Specifically, Graph-Patchformer embeds structural encodings into MTS to reflect the inter-series relationships and temporal variations within the MTS. The embedded data is subsequently fed into the Patch Interaction Blocks through a patching operation. Within the Patch Interaction Blocks, the multi-head self-attention mechanism and adaptive graph learning module are employed to capture intra-series dependencies and inter-series local dynamic dependencies. In this way, Graph-Patchformer not only facilitates interactions between different patches within a single series but also enables cross-time-window interactions between patches of different series. The experimental results show that the Graph-Patchformer outperforms the state-of-the-art approaches and exhitits significant forecasting performance compared to several state-of-the-art methods across various real-world benchmark datasets. The code will be available at this repository: https://github.com/houchunyiPhd/Graph-Patchformer/tree/main.