An Efficient Solution for Multivariate Time Series Forecasting Based on a Stacked Complex Fuzzy Gated Recurrent Neural Network

Multivariate Time series forecasting finds numerous applications across various fields, including society, industry, market, etc. Recently, gated recurrent unit neural networks (GRU) have shown high efficiency in processing sequential time series data in recent years. While traditional GRUs can learn and understand time series data, with the explosion and increasing complexity of data, there has not been much research on GRU networks that considers the fuzziness and periodicity of the data's nature. Thus, the novel developed complex fuzzy-gated recurrent neural network (CFGRU) is proposed in this study to improve the ability of GRU networks to resolve multivariate time series forecasting issues. Complex fuzzy theory, which represents the uncertainty and periodicity of the data space from the input data, is integrated with GRU regression neural networks and the proposed CFGRU network. Furthermore, this paper also suggests a stacked residual complex fuzzy-gated recurrent neural network architecture for multivariate time series data forecasting. An experiment was carried out on multivariate time series data sets comprising 05 multivariate time series datasets (weather, sunspots, PM2.5, air quality, and power consumption) to validate the success and efficiency of the suggested model. Comparison results on three indices-MAE, RMSE, and SMAPE-indicate that the proposed model performs forecasting better than both complex fuzzy forecasting models and conventional GRU models.