A hybrid time series forecasting model combining recurrent neural networks and ensemble learning for furniture sales prediction

This study proposes a deep learning model, named MP-LRNet, that aims to improve the accuracy and stability of time series forecasting by combining recurrent neural networks and ensemble learning algorithms within a modular Multi-Patch structure. The model is designed to learn temporal patterns at different time intervals and to capture both short- and long-period dependencies in sequential data. Accurate time series forecasting plays a central role in supporting decisions across various practical domains such as retail, production, and energy management. To evaluate the performance of MP-LRNet, experiments were conducted using a real furniture sales dataset and a publicly available energy consumption benchmark (UCI Household Electric Power Consumption). The proposed model achieved an R 2 value of 0.9918, demonstrating reliable predictive ability and consistent results across different configurations. The Multi Patch structure enhanced temporal representation, while integrating long short-term memory and Random Forests improved predictive precision without a significant increase in computational time. The findings indicate that MP-LRNet serves as an effective approach for sales prediction and energy demand estimation, suggesting strong potential to be adapted for broader diverse applications, such as environmental analysis, in future studies.