Gated Hybrid Network Using CRNN and RNN Models for Time-Series Forecasting and Case Studies

Time-series forecasting is a critical challenge in industrial applications such as inventory management and energy planning. This paper proposes a gated hybrid network for time-series forecasting that combines feature vectors obtained from convolutional recurrent neural network (CRNN) and recurrent neural network (RNN) branches. Unlike conventional CRNN models, the proposed model feeds the original time-series sequence in parallel to both CRNN and independent RNN branches, enabling each branch to extract complementary temporal features. A squeeze-and-excitation-inspired gating mechanism then adaptively weights and integrates the two feature vectors. Experiments were conducted using six real-world datasets, including automotive sales, electricity consumption, and solar energy data. The proposed model achieved lower mean relative squared error and relative absolute error values averaged across four forecasting periods than autoregressive (AR), autoregressive integrated moving average (ARIMA), Holt–Winters, gated recurrent unit (GRU), and long short-term memory (LSTM) baseline models on most of the datasets, while also demonstrating superior prediction stability. These results suggest that the proposed architecture is suitable for practical industrial forecasting tasks.