Predictive Error Compensating Wavelet Neural Network Model for Multivariable Time Series Prediction.
- Resource Type
- Article
- Authors
- Kulaglic, Ajla; Ustundag, B. Berk
- Source
- TEM Journal. Nov2021, Vol. 10 Issue 4, p1955-1963. 9p.
- Subject
- *ARTIFICIAL neural networks
*TIME series analysis
*DATA fusion (Statistics)
*FORECASTING
*WIND speed
*MACHINE learning
- Language
- ISSN
- 2217-8309
Multivariable machine learning (ML) models are increasingly used for time series predictions. However, avoiding the overfitting and underfitting in ML-based time series prediction requires special consideration depending on the size and characteristics of the available training dataset. Predictive error compensating wavelet neural network (PEC-WNN) improves the time series prediction accuracy by enhancing the orthogonal features within a data fusion scheme. In this study, time series prediction performance of the PEC-WNNs have been evaluated on two different problems in comparison to conventional machine learning methods including the long short-term memory (LSTM) network. The results have shown that PECNET provides significantly more accurate predictions. RMSPE error is reduced by more than 60% with respect to other compared ML methods for Lorenz Attractor and wind speed prediction problems. [ABSTRACT FROM AUTHOR]