Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven

TL;DR

This paper introduces ACNet, an adaptive convolutional network that utilizes multi-resolution and deformable convolutions to better capture nonlinear features and temporal dependencies in multivariate time series, improving forecasting accuracy.

Contribution

The paper proposes a novel ACNet architecture that combines multi-resolution and deformable convolutions for enhanced nonlinear feature extraction in time series forecasting.

Findings

ACNet outperforms existing models on twelve real-world datasets.

ACNet achieves state-of-the-art accuracy in short-term and long-term forecasting.

ACNet maintains favorable runtime efficiency.

Abstract

Time series data in real-world scenarios contain a substantial amount of nonlinear information, which significantly interferes with the training process of models, leading to decreased prediction performance. Therefore, during the time series forecasting process, extracting the local and global time series patterns and understanding the potential nonlinear features among different time observations are highly significant. To address this challenge, we introduce multi-resolution convolution and deformable convolution operations. By enlarging the receptive field using convolution kernels with different dilation factors to capture temporal correlation information at different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture potential nonlinear features among time observations. Building upon this, we propose ACNet, an adaptive convolutional network designed to effectively model the local and global temporal dependencies and the nonlinear features between observations in multivariate time series. Specifically, by extracting and fusing time series features at different resolutions, we capture both local contextual information and global patterns in the time series. The designed nonlinear feature adaptive extraction module captures the nonlinear features among different time observations in the time series. We evaluated the performance of ACNet across twelve real-world datasets. The results indicate that ACNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks with favorable runtime efficiency.