Research on E-Commerce Inventory Sales Forecasting Model Based on ARIMA and LSTM Algorithm

Accurate forecasting is critical for effective warehouse network planning and inventory management in e-commerce. This study tackles these challenges by applying a differentiated forecasting strategy over a three-month period. The Autoregressive Integrated Moving Average (ARIMA) model is used for monthly inventory predictions, while the Long Short-Term Memory (LSTM) neural network is employed for daily sales forecasts. Experimental validation across 350 product categories demonstrates the efficacy of this approach. ARIMA effectively captured dynamic inventory trends (e.g., Category 1 showing gradual increases; Category 91 depleting from 3824 to 0). Concurrently, LSTM successfully modeled complex daily sales fluctuations (e.g., Category 61 peaking at 3693 units on 21 July; Category 31 consistently recording zero sales). This dual-model strategy, leveraging the complementary strengths of ARIMA for relatively stable monthly inventory series and LSTM for volatile daily sales patterns, provides a robust, data-driven basis for optimizing warehouse resource planning and product category allocation. Furthermore, visualization of categorized forecast results reveals distinct sales distribution patterns, thereby enabling enterprises to refine inventory and sales strategies with greater precision, leading to reduced redundant space investment and improved resource allocation efficiency. Future research will focus on incorporating multivariate interactions to further enhance model practicality and predictive power.