Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform

Time series forecasting, the process of predicting future values based on observed historical data, is a pivotal task in various fields such as economics, finance, medicine, and environmental science. This task becomes particularly complex when dealing with multivariate temporal data, where predicting future values involves understanding the intricate interdependencies among multiple variables. This is essential in scenarios like weather forecasting, where variables such as temperature and humidity are interlinked, or in financial markets, where the stock prices of interconnected companies are observed. Recent advancements in time series forecasting have transitioned from conventional statistical approaches [1, 2] to sophisticated machine learning techniques, notably deep learning [3, 4, 5]. However, the field still grapples with a scarcity of both precise and interpretable models for multivariate time series prediction.