Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window

TL;DR

This paper introduces a semi-asymmetric convolutional model with a global atrous sliding window for time series forecasting, effectively capturing long-term and local-global relationships to improve prediction accuracy.

Contribution

It proposes a novel semi-asymmetric convolution and a global atrous sliding window to better extract local and global features in time series data.

Findings

Achieves state-of-the-art performance on multiple datasets.

Effectively captures long-term dependencies.

Enhances prediction accuracy over existing models.

Abstract

The proposed method in this paper is designed to address the problem of time series forecasting. Although some exquisitely designed models achieve excellent prediction performances, how to extract more useful information and make accurate predictions is still an open issue. Most of modern models only focus on a short range of information, which are fatal for problems such as time series forecasting which needs to capture long-term information characteristics. As a result, the main concern of this work is to further mine relationship between local and global information contained in time series to produce more precise predictions. In this paper, to satisfactorily realize the purpose, we make three main contributions that are experimentally verified to have performance advantages. Firstly, original time series is transformed into difference sequence which serves as input to the proposed model. And secondly, we introduce the global atrous sliding window into the forecasting model which references the concept of fuzzy time series to associate relevant global information with temporal data within a time period and utilizes central-bidirectional atrous algorithm to capture underlying-related features to ensure validity and consistency of captured data. Thirdly, a variation of widely-used asymmetric convolution which is called semi-asymmetric convolution is devised to more flexibly extract relationships in adjacent elements and corresponding associated global features with adjustable ranges of convolution on vertical and horizontal directions. The proposed model in this paper achieves state-of-the-art on most of time series datasets provided compared with competitive modern models.