Recurrent Penalized Splines for Real-Time Forecasting of the Parameters of Technical Systems

Forecasting time series (TS) from their historical data is used in many preventive monitoring and decision support systems in industry. For systems operating in real-time, an urgent problem is to reduce forecasting time without compromising other quality indexes (accuracy and trend continuation). Th...

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Bibliographic Details
Published in2023 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) pp. 1019 - 1023
Main Authors Kochegurova, Elena, Kaida, Anastasiia, Galkina, Maria
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.05.2023
Subjects
Computational modeling
Data models
Heuristic algorithms
Mathematical models
penalty P-spline
performance indicators
Predictive models
real time
Real-time systems
Time series analysis
time series prediction
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Summary:Forecasting time series (TS) from their historical data is used in many preventive monitoring and decision support systems in industry. For systems operating in real-time, an urgent problem is to reduce forecasting time without compromising other quality indexes (accuracy and trend continuation). The purpose of this paper is to describe a forecasting model based on a recurrent penalized P-spline. The spline model has a compact computational scheme where the efficiency is achieved by using a short input of historical data a simple mathematical description. The model was compared to 11 most well-known forecasting algorithms. The performance of the proposed model compared quite well to the competitive forecasting algorithms. The model was used to forecast a real-world TS of a technological parameter of a boiler plant.
DOI:10.1109/ICIEAM57311.2023.10139224