The accurate prediction of failure events is of central interest to the field of predictive maintenance, where the role of forecasting is of paramount importance. In this paper, we present and compare some advanced statistical and machine learning methods for multi-step multivariate time series forecasting. Regarding statistical methods, we considered VAR, VMA, VARMA and Theta. The machine learning approaches we selected are variants of the Recurrent Neural Network model, namely ERNN, LSTM and GRU. All the considered methods have been evaluated in terms of accuracy, using 5 public datasets. As an extra contribution, we have implemented the multivariate version of the Theta method.

Advanced statistical and machine learning methods for multi-step multivariate time series forecasting in predictive maintenance / Tessoni, Valentina; Amoretti, Michele. - (2022). (Intervento presentato al convegno 3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 nel 19-21/11/2021) [10.1016/j.procs.2022.01.273].

Advanced statistical and machine learning methods for multi-step multivariate time series forecasting in predictive maintenance

Valentina Tessoni;Michele Amoretti
2022-01-01

Abstract

The accurate prediction of failure events is of central interest to the field of predictive maintenance, where the role of forecasting is of paramount importance. In this paper, we present and compare some advanced statistical and machine learning methods for multi-step multivariate time series forecasting. Regarding statistical methods, we considered VAR, VMA, VARMA and Theta. The machine learning approaches we selected are variants of the Recurrent Neural Network model, namely ERNN, LSTM and GRU. All the considered methods have been evaluated in terms of accuracy, using 5 public datasets. As an extra contribution, we have implemented the multivariate version of the Theta method.
2022
Advanced statistical and machine learning methods for multi-step multivariate time series forecasting in predictive maintenance / Tessoni, Valentina; Amoretti, Michele. - (2022). (Intervento presentato al convegno 3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 nel 19-21/11/2021) [10.1016/j.procs.2022.01.273].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2930751
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