Comparison of Hybrid Recurrent Neural Networks for Univariate Time Series Forecasting

Abstract

The work presented in this paper aims to improve the accuracy of forecasting models in univariate time series, for this it is experimented with different hybrid models of two and four layers based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). It is experimented with two time series corresponding to downward thermal infrared and all sky insolation incident on a horizontal surface obtained from NASA’s repository. In the first time series, the results achieved by the two-layer hybrid models (LSTM + GRU and GRU + LSTM) outperformed the results achieved by the non-hybrid models (LSTM + LSTM and GRU + GRU); while only two of six four-layer hybrid models (GRU + LSTM + GRU + LSTM and LSTM + LSTM + GRU + GRU) outperformed non-hybrid models (LSTM + LSTM + LSTM + LSTM and GRU + GRU + GRU + GRU). In the second time series, only one model (LSTM + GRU) of two hybrid models outperformed the two non-hybrid models (LSTM + LSTM and GRU + GRU); while the four-layer hybrid models, none could exceed the results of the non-hybrid models.