ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning

• URL: http://arxiv.org/abs/2310.09488v1
• Date: Sat, 14 Oct 2023 04:37:38 GMT
• Title: ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
• Authors: Jiecheng Lu, Xu Han, Shihao Yang
• Abstract summary: ARM is a multivariate temporal-contextual adaptive learning method. It better handles individual series temporal patterns and correctly learns inter-series dependencies. ARM demonstrates superior performance on multiple benchmarks without significantly increasing computational costs.
• Score: 8.680653542513392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships. As multivariate input models underperforming some recent univariate counterparts, we posit that the issue lies in the inefficiency of existing multivariate LTSF Transformers to model series-wise relationships: the characteristic differences between series are often captured incorrectly. To address this, we introduce ARM: a multivariate temporal-contextual adaptive learning method, which is an enhanced architecture specifically designed for multivariate LTSF modelling. ARM employs Adaptive Univariate Effect Learning (AUEL), Random Dropping (RD) training strategy, and Multi-kernel Local Smoothing (MKLS), to better handle individual series temporal patterns and correctly learn inter-series dependencies. ARM demonstrates superior performance on multiple benchmarks without significantly increasing computational costs compared to vanilla Transformer, thereby advancing the state-of-the-art in LTSF. ARM is also generally applicable to other LTSF architecture beyond vanilla Transformer.

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