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A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm
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DOI：10.1109/TSMC.2017.2712184.png)
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Time-series prediction is important in diverse fields. Traditionally, methods for time-series prediction were based on fixed linear models because of mathematical tractability. Researchers turned their attention to artificial neural networks due to their better approximation capability. In this paper, we use feedforward neural networks with a single hidden layer, and present a rather simple online sequential learning algorithm (OSLA) together with its proof. The convergence properties of this algorithm are those of the well-known recursive least squares algorithm. We demonstrate that the prediction performance is better than other OSLAs, and show that it is statistically different from them. In addition, we also present the multiple models, switching, and tuning methodology that enhances the prediction performance of the learning algorithm.
Keywords:
Prediction models
recurrent neural networks
supervised learning
time-series analysis
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