Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models

Time series forecasting plays a crucial role in various industries, particularly in predicting product demand for effective supply chain management. This paper presents a novel approach to time series forecasting by leveraging advanced deep learning techniques, specifically focusing on hybrid models that combine attention mechanisms with traditional recurrent neural networks. Our proposed method, the Hybrid Attention-based Long Short-Term Memory (HA-LSTM) network, integrates multi-head self-attention modules with LSTM layers to capture both long-term dependencies and local temporal patterns in time series data. We evaluate our model on a large-scale retail dataset from the “Predict Future Sales” competition, demonstrating its effectiveness in handling complex, real-world time series with multiple seasonalities and trends. Experimental results show that the HA-LSTM outperforms state-of-the-art baselines, including ARIMA, Prophet, and vanilla LSTM models, achieving a 15% improvement in Mean Absolute Percentage Error (MAPE) and a 12% reduction in Root Mean Square Error (RMSE). Furthermore, we conduct an extensive ablation study to analyze the contribution of each component in our hybrid model. The proposed approach not only enhances forecasting accuracy but also provides interpretable attention weights, offering insights into the relative importance of different time steps in making predictions. This research contributes to the growing body of work on deep learning for time series analysis and offers practical implications for improving demand forecasting in retail and supply chain management.