Comparative Study of Sales Forecasting using LSTM and ARIMA Methods

The current competitive manufacturing environment drives many companies to respond quickly to demand. One effective approach is to predict future market conditions. In this research, sales predictions were carried out using a case study of a lunch box manufacturing company. The company requires a method to predict lunch box sales to estimate the number of products to be produced. This aims to prevent excessive overproduction or underproduction. The prediction methods used in this research were Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA). The LSTM method tends to be better suited to non-linear data such as market conditions, while ARIMA was used for comparison. Based on the prediction results for the company's two products, the LSTM method performed better than ARIMA in all assessment types: Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE).