DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang
Abstract
Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly.
Topics & Concepts
Cluster analysisMultivariate statisticsComputer scienceDual (grammatical number)Series (stratigraphy)Time seriesData miningArtificial intelligenceMachine learningArtLiteratureBiologyPaleontologyTime Series Analysis and ForecastingStock Market Forecasting MethodsForecasting Techniques and Applications