Bootstrapping Neural Networks for Time Series Forecasting
Simon Löfwander · 2018
In this study artificial neural networks (ANNs) are utilized for time series forecasting. Analyzed time series includes two Google AdWords metrics and revenue for an e-commerce. Classes of ANNs included in the analysis are feedforward multilayer perceptron’s and two types of recurrent neural networks (RNNs), specifically Jordan- and Elman neural networks. Since ANNs lack econometric interpretation and yield single-point predictions, focus is put on implementing a parametric bootstrapping to create prediction intervals for the forecasts. The constructed prediction intervals were successful in containing all observations in validation data sets for each of the studied time series. The analyzed time series exhibited similar characteristics, which resulted in a comparable set of predictor variables in the final models, while they differed individually in terms of best performing ANN-class. (Less)