A Method for Selecting Learning Data in the Prediction of Time Series with Explanatory Variables using Neural Networks
Hisashi Shimodaira · Industrial and Engineering Applications of Artificial Intelligence and Expert Systems · 2022
In the prediction of time series using multilayer feedforward neural networks, there are two practical methods for selecting learning data: the moving window data learning method and the similar data selective learning method with the correlation coefficient based similar data selection method which we proposed in a previous paper. In this paper, for time series data with explanatory variables, the predictive performance by the two methods was investigated by numerical simulations. With the time series whose nature is choppy, that by the latter was considerably better than that by the former. With the time series whose nature is smooth, that by the former was slightly better than that by the latter. According to these results, it is conjectured that the latter is effective for a time series whose nature is choppy.