A Study on Pork Price Prediction Using LSTM

Abstract

In this study, we propose LSTM, a technique for predicting pork prices to stabilize the supply and demand of pork. The LSTM model is trained using the attributes of day and month as additional inputs to historical time series data. The performance of the proposed LSTM model was compared to that of ARIMA, SARIMA, autoARIMA, and standard LSTM in terms of three performance metrics: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). As a result of the experiment, the error rate of the proposed LSTM model was significantly lower than that of other existing methods. In future research, it will be necessary to redefine the analysis of causal relationships such as livestock substitutes and supplements, and to conduct practical studies such as linking OpenAPI with big data of public institutions to improve the accuracy of price prediction.