Pattern matching for multivariate time series forecasting
Résumé
This article presents a new approach to multivariate time series forecasting. While most existing techniques in the literature focus on forecasting a single time series, forecasting multiple time series is a common goal in many applications. To deal with this, we introduce a new method, Weighted Nearest Neighbours for multivariate time series ($WNN_{multi}$). This method forecasts the future of a given series by identifying similar patterns not only in its own past but also in the past of related time series. Once the $k$ nearest neighbours are identified, forecasts are made by averaging their future values. We evaluate the proposed approach on several real-world datasets and compare its performance against state-of-the-art forecasting techniques. The results demonstrate that our method achieves comparable or significantly improved performance, showcasing its effectiveness.
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