Abstract
Multivariate Time Series (MTS) involve multiple time series variables that are interdependent. The MTS follows two dimensions, namely spatial along the different variables composing the MTS and temporal. Both, the complex and the time-evolving nature of MTS data make forecasting one of the most challenging tasks in time series analysis. Typical methods for MTS forecasting are designed to operate in a static manner in time or space without taking into account the evolution of spatio-temporal dependencies among data observations, which may be subject to significant changes. Moreover, it is generally accepted that none of these methods is universally valid for every application. Therefore, we propose an online adaptation of MTS forecasting by devising a fully automated framework for both adaptive input spatio-temporal variables and adequate forecasting model selection. The adaptation is performed in an informed manner following concept-drift detection in both spatio-temporal dependencies and model performance over time. In addition, a well-designed meta-learning scheme is used to automate the selection of appropriate dependence measures and the forecasting model. An extensive empirical study on several real-world datasets shows that our method achieves excellent or on-par results in comparison to the state-of-the-art (SoA) approaches as well as several baselines.
This work is supported by the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 and the Federal Ministry of Education and Research of Germany as part of the competence center for machine learning ML2R (01-S18038A).
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Saadallah, A., Mykula, H., Morik, K. (2023). Online Adaptive Multivariate Time Series Forecasting. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_2
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