FORECAST OF MULTIVARIATE TIME SERIES SAMPLED FROM INDUSTRIAL MACHINERY SENSORS
Goal: To evaluate the performance of a set of forecasting methods in the prediction of future values on a dataset of time series collected from sensors installed in an industrial gas turbine.
Design/Methodology/Approach: Forecasting methods tested include the use of multivariate and univariate neural networks (FNN and LSTM), exponential smoothing and ARIMA models.
Results: Results show that the use of ARIMA models to forecast on the dataset is the best default method to apply, and is the only forecasting method that consistently beats a simple naïve no-change model.
Limitation of the investigation: There was a focus on evaluating neural networks. This limited resources available to evaluate other forecasting methods. There is no guarantee that it would not be possible to find neural networks capable of yielding better forecasts than the ones achieved by the best performing methods in this research.
Practical implications: The broadest possible implications of the results are that the best default method to forecast industrial machinery time series is the use of ARIMA models. Additionally, neural networks are not capable of beating methods well stablished within the forecasting community, namely ARIMA models.
Originality/Value: To the best of the authors’ knowledge, there is a scarce amount of published evaluations of multiple forecasting methods on data from real machines. This knowledge is useful for the understanding of the best forecasting methods available for the estimation of machine’s RUL using sensor time series.
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