TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting

TL;DR

This paper introduces TCCT, a tightly-coupled convolutional Transformer architecture for time series forecasting, which enhances efficiency and local feature extraction, outperforming existing models with lower computational costs.

Contribution

The paper proposes a novel tightly-coupled convolutional Transformer (TCCT) framework with three architectures that improve efficiency and accuracy in time series forecasting.

Findings

TCCT reduces self-attention computation by 30% and memory by 50%.

TCCT architectures outperform state-of-the-art models on real datasets.

The proposed methods achieve higher accuracy with lower computational costs.

Abstract

Time series forecasting is essential for a wide range of real-world applications. Recent studies have shown the superiority of Transformer in dealing with such problems, especially long sequence time series input(LSTI) and long sequence time series forecasting(LSTF) problems. To improve the efficiency and enhance the locality of Transformer, these studies combine Transformer with CNN in varying degrees. However, their combinations are loosely-coupled and do not make full use of CNN. To address this issue, we propose the concept of tightly-coupled convolutional Transformer(TCCT) and three TCCT architectures which apply transformed CNN architectures into Transformer: (1) CSPAttention: through fusing CSPNet with self-attention mechanism, the computation cost of self-attention mechanism is reduced by 30% and the memory usage is reduced by 50% while achieving equivalent or beyond prediction accuracy. (2) Dilated causal convolution: this method is to modify the distilling operation proposed by Informer through replacing canonical convolutional layers with dilated causal convolutional layers to gain exponentially receptive field growth. (3) Passthrough mechanism: the application of passthrough mechanism to stack of self-attention blocks helps Transformer-like models get more fine-grained information with negligible extra computation costs. Our experiments on real-world datasets show that our TCCT architectures could greatly improve the performance of existing state-of-art Transformer models on time series forecasting with much lower computation and memory costs, including canonical Transformer, LogTrans and Informer.