CVTN: Cross Variable and Temporal Integration for Time Series Forecasting

TL;DR

This paper introduces CVTN, a novel Transformer-based framework that separates feature mining and temporal dependency learning in multivariate time series forecasting, achieving state-of-the-art results.

Contribution

The paper proposes a new two-phase approach, dividing cross-variable and cross-time learning to improve feature extraction and reduce overfitting in time series forecasting.

Findings

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

Effectively separates feature mining from temporal dependency learning.

Demonstrates robustness across various real-world scenarios.

Abstract

In multivariate time series forecasting, the Transformer architecture encounters two significant challenges: effectively mining features from historical sequences and avoiding overfitting during the learning of temporal dependencies. To tackle these challenges, this paper deconstructs time series forecasting into the learning of historical sequences and prediction sequences, introducing the Cross-Variable and Time Network (CVTN). This unique method divides multivariate time series forecasting into two phases: cross-variable learning for effectively mining fea tures from historical sequences, and cross-time learning to capture the temporal dependencies of prediction sequences. Separating these two phases helps avoid the impact of overfitting in cross-time learning on cross-variable learning. Exten sive experiments on various real-world datasets have confirmed its state-of-the-art (SOTA) performance. CVTN emphasizes three key dimensions in time series fore casting: the short-term and long-term nature of time series (locality and longevity), feature mining from both historical and prediction sequences, and the integration of cross-variable and cross-time learning. This approach not only advances the current state of time series forecasting but also provides a more comprehensive framework for future research in this field.