Impact of Multistep Forecasting Strategies on Recurrent Neural Networks Performance for Short and Long Horizons
Rohaifa Khaldi, Abdellatif El Afia, Raddouane Chiheb · 2019
Forecasting is one of the most important tasks in temporal data mining. Actually, most of forecasting applications require performing multistep forecasts. Thus, what is the appropriate multistep forecasting strategy to use with each model? To answer this question, this study evaluates the impact of multistep forecasting strategies on the performance and the stability of recurrent neural networks models (SRN, LSTM and GRU) for short term and long term horizons. This comparison is based on well-defined benchmarking univariate time series (deterministic, stochastic and chaotic), whose properties are known and challenged their modeling.