Research and application of commodity demand forecasting algorithm based on improved ARIMA-LSTM

Through thorough investigation and research, it has been determined that commodities possess a certain degree of timeliness, with long production cycles and short storage times. Accurately predicting customer demand can effectively reduce costs. This paper proposes an improved model based on ARIMA-LSTM, incorporating data cross-correlation analysis for effective data classification. By leveraging the unique characteristics of both ARIMA and LSTM prediction time series models, the data is divided into stationary and non-stationary models for accurate forecasting of future demand for goods. After rigorous testing on the test set, our proposed model achieved impressive results with an accuracy rate of 0.855, precision rate of 0.834, and recall rate of 0.854 respectively. Therefore, the model proposed in this paper has a good performance in forecasting the sales volume of general commodities with time series.