Nonlinear hysteretic parameter identification using an attention-based long short-term memory network and principal component analysis

Ding, Zhenghao; Yu, Yang; Xia, Yong

doi:10.1007/s11071-022-08095-x

Cited by 6 publications

(1 citation statement)

References 42 publications

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“…The authors [25][26][27][28] describe the application of time series with seasonal variability in industry and energy and resource consumption. The application of deep learning technology and long short-term memory network for physical systems is shown in [29,30]. All research above uses practically the same set of methods and algorithms.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Forecasting of Seasonal Data Using Machine Learning Methods

Kramar

Alchakov

2023

Algorithms

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The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant can be used to calculate the optimal electricity consumption. The article describes a performance analysis of various machine learning methods (SARIMA, Holt-Winters Exponential Smoothing, ETS, Facebook Prophet, XGBoost, and Long Short-Term Memory) and data-preprocessing algorithms implemented in Python. The general methodology of model building and the requirements of the input data sets are described. All models use actual data from sensors of the monitoring system. The novelty of this work is in an approach that allows using limited history data sets to obtain predictions with reasonable accuracy. The implemented algorithms made it possible to achieve an R-Squared accuracy of more than 0.95. The forecasting calculation time is minimized, which can be used to run the algorithm in real-time control and embedded systems.

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