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• W3199125616 abstract "The multivariate time series forecasting has attracted more and moreattention because of its vital role in different fields in the real world, suchas finance, traffic, and weather. In recent years, many research efforts havebeen proposed for forecasting multivariate time series. Although some previouswork considers the interdependencies among different variables in the sametimestamp, existing work overlooks the inter-connections between differentvariables at different time stamps. In this paper, we propose a simple yetefficient instance-wise graph-based framework to utilize the inter-dependenciesof different variables at different time stamps for multivariate time seriesforecasting. The key idea of our framework is aggregating information from thehistorical time series of different variables to the current time series thatwe need to forecast. We conduct experiments on the Traffic, Electricity, andExchange-Rate multivariate time series datasets. The results show that ourproposed model outperforms the state-of-the-art baseline methods." @default.
• W3199125616 created "2021-09-27" @default.
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• W3199125616 date "2021-09-14" @default.
• W3199125616 modified "2023-09-23" @default.
• W3199125616 title "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting" @default.
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