An Evolving Autoregressive Predictor for Time Series Forecasting
- 1 University of Waterloo, Canada
- 2 Lakehead University, Canada
Copyright: © 2020 De Z. Li, Wilson Wang and Fathy Ismail. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Autoregressive (AR) model is a common predictor that has been extensively used for time series forecasting. Many training methods can used to update AR model parameters, for instance, least square estimate and maximum likelihood estimate; however, both techniques are sensitive to noisy samples and outliers. To deal with the problems, an evolving AR predictor, EAR, is developed in this study to enhance prediction accuracy and mitigate the effect of noisy samples and outliers. The model parameters of EAR are trained with an Adaptive Least Square Estimate (ALSE) method, which can learn samples characteristics more effectively. In each training epoch, the ALSE weights the samples by their fitting accuracy. The samples with larger fitting errors will be given a larger penalty value in the cost function; however the penalties of difficult-to-predict samples will be adaptively reduced to enhance the prediction accuracy. The effectiveness of the developed EAR predictor is verified by simulation tests. Test results show that the proposed EAR predictor can capture the dynamics of the time series effectively and predict the future trend accurately.
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- Autoregressive Model
- Adaptive Least Square Estimate
- Time Series Forecasting