DAMLP: Data Augmented Multi-Layer Perceptrons for Multivariate Time Series Forecasting

Multivariate time series forecasting MTSF plays a crucial role in various applications by predicting future values based on historical data across multiple variates. Although deep learning models have achieved remarkable success in MTSF tasks, they often face challenges related to data scarcity. Data augmentation, which enriches the training data, has emerged as a promising technique to improve forecasting accuracy. However, preserving temporal dependencies in augmented data remains a significant challenge. In this paper, we introduce Data Augmented Multi-Layer Perceptrons DAMLP , a novel MTSF framework that integrates a Data Augmentation DA module and a simple yet effective Multi-Layer Perceptrons MLP architecture. Our DA module enhances the training dataset by increasing the frequency of time series with high correlations to others while reducing the frequency of low-correlation series, thus mitigating the interference on the model's forecasting accuracy caused by low-correlation series. To efficiently utilize the augmented dataset, we use a simple MLP architecture that provides an efficient solution without sacrificing forecasting performance. Our experimental results on multiple real-world datasets demonstrate that DAMLP outperforms state-of-the-art models with less memory usage and training time. Our approach highlights the potential of leveraging correlation information to improve the accuracy and efficiency of MTSF models.