Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2308.11946v1
• Date: Wed, 23 Aug 2023 06:40:05 GMT
• Title: Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
• Authors: Yifan Zhang, Rui Wu, Sergiu M. Dascalu, Frederick C. Harris Jr
• Abstract summary: We introduce a dimension invariant embedding technique that captures short-term temporal dependencies. We present a novel Multi-scale Transformer Pyramid Network (MTPNet) specifically designed to capture temporal dependencies at multiple unconstrained scales.
• Score: 8.739572744117634
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multivariate Time Series (MTS) forecasting involves modeling temporal dependencies within historical records. Transformers have demonstrated remarkable performance in MTS forecasting due to their capability to capture long-term dependencies. However, prior work has been confined to modeling temporal dependencies at either a fixed scale or multiple scales that exponentially increase (most with base 2). This limitation hinders their effectiveness in capturing diverse seasonalities, such as hourly and daily patterns. In this paper, we introduce a dimension invariant embedding technique that captures short-term temporal dependencies and projects MTS data into a higher-dimensional space, while preserving the dimensions of time steps and variables in MTS data. Furthermore, we present a novel Multi-scale Transformer Pyramid Network (MTPNet), specifically designed to effectively capture temporal dependencies at multiple unconstrained scales. The predictions are inferred from multi-scale latent representations obtained from transformers at various scales. Extensive experiments on nine benchmark datasets demonstrate that the proposed MTPNet outperforms recent state-of-the-art methods.

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