Strategies for multi-step-ahead available parking spaces forecasting based on wavelet transform

Abstract

A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform (WT), artificial neural network (ANN) and forecasting strategies based on the changing characteristics of available parking spaces (APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output (MIMO) strategy, DIRMO strategy (a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output (RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.