Kanokrot Phuruan, Chompoonoot Kasemset

Published in Data Science and Engineering (DSE) Record 2022 Vol. 3 No. 1 pp. 35-48


Shallot is one of important agricultural products exported with high vol-ume to many countries. Shallots are mainly cultivated at the northern region of Thailand. Price of shallot in different periods during a year is changed from many related parameters. This research aimed to develop the forecast-ing model of shallot’s price using combination techniques from ARIMA and LSTM (ARIMA-LSTM). Considering independent parameters, ARIMA was applied for predicting effects from parameters with time-series and lin-ear relationship, whereas LSTM was applied for predicting effects from pa-rameters with non-linear relationship. Data collected 84 months during January 2014 to December 2020 were applied in this research. The accuracy of the proposed model was evaluated using three indicators including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Abso-lute Percentage Error (MAPE). The results presented that our ARIMA-LSTM model gave minimum values of RMSE, MAE and MAPE as 10.275 Baht, 8.512 Baht, and 13.618%, respectively. Moreover, the value of MAPE was in good forecasting level that can be implemented practically.