TSI: A Multi-View Representation Learning Approach for Time Series Forecasting

TL;DR

This paper presents TSI, a multi-view learning approach that combines trend, seasonal, and ICA-based representations to improve multivariate time series forecasting accuracy and understanding.

Contribution

It introduces a novel multi-view framework integrating trend, seasonal, and ICA representations, advancing the modeling of complex, high-dimensional time series data.

Findings

TSI outperforms state-of-the-art models on benchmark datasets.

The approach enhances forecasting accuracy for multivariate time series.

Provides deeper insights into nonlinear relationships in time series data.

Abstract

As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting becomes increasingly crucial across various domains. This is highlighted by recent advancements in representation learning within the field. This study introduces a novel multi-view approach for time series forecasting that innovatively integrates trend and seasonal representations with an Independent Component Analysis (ICA)-based representation. Recognizing the limitations of existing methods in representing complex and high-dimensional time series data, this research addresses the challenge by combining TS (trend and seasonality) and ICA (independent components) perspectives. This approach offers a holistic understanding of time series data, going beyond traditional models that often miss nuanced, nonlinear relationships. The efficacy of TSI model is demonstrated through comprehensive testing on various benchmark datasets, where it shows superior performance over current state-of-the-art models, particularly in multivariate forecasting. This method not only enhances the accuracy of forecasting but also contributes significantly to the field by providing a more in-depth understanding of time series data. The research which uses ICA for a view lays the groundwork for further exploration and methodological advancements in time series forecasting, opening new avenues for research and practical applications.