Time Series Forecasting with GCN-LSTM Based Unified Model for Product Demand Prediction

This paper introduces LSTMGraph, a unified time-series model designed for demand prediction across multiple products. This method integrates Long Short-Term Memory (LSTM) networks to capture temporal dynamics, such as price fluctuations, and Graph Convolutional Networks (GCN) to model global dependencies between products. We represent demand data as a network where each product is a node, constructing three distinct graphs with different types of edges: (i) a weekly sales similarity graph, (ii) a customer-based relationship graph, and (iii) an invoice-based similarity graph. These graphs are merged to enhance predictive accuracy by incorporating diverse temporal and relational patterns. Extensive experiments show that LSTMGraph significantly outperforms existing baseline models. Additionally, an ablation study is conducted to quantify the impact of each graph type on overall performance.