Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series

Mikhail Sarafanov, Valerii Pokrovskii, Nikolay O. Nikitin

Abstract: In the paper, we discuss the applicability of automated machine learning for the effective multi-scale modeling of the industrial sensors time series. The proposed approach is based on the evolutionary generative design of the composite modeling pipelines. The iterative data decomposition algorithm is proposed in the paper to improve the quality of the sensor time series forecasting. To effectively use it in an automated way, the boosting-like mutation operators have been implemented for graphs-based genotypes. The proposed approach reduced the forecast error by 10% compared to the competitor library AutoTS. Also, the proposed modifications of the evolutionary algorithm resulted in better metrics in 78% of the cases where they were used.