TY - GEN
T1 - Gum Rosin Price Forecasting Using A Hybrid ARIMA - LSTM Model
AU - Rasyad, Muhammad Naufal
AU - Tyasnurita, Raras
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The agricultural sector is one of Indonesia's economic strengths. It can be seen from the various kinds of agricultural commodities. As the largest agrarian country in the world, Indonesia can produce agricultural products up to 14.7% - 15% of the total gross domestic product. One of the largest agricultural products in Indonesia is gondorukem (Gum Rosin). Rosin is the sap from this pine tree. Gum rosin exports for 2012 were in response to the increasing international market demand and prices tended to rise from 1,300 USD to 1,500 USD/ton. On January 1, 2019, the price of this commodity became 1,050 USD/ton. It shows that the price of agricultural commodities is very volatile. With this condition, there is concern from both buyer and producer community. There is a difficulty in buying and selling gum rosin. To overcome this, forecasting is applied to predict the estimated price of this commodity. The forecasting model of Autoregressive Integrated Moving Average (ARIMA) and Long-Short Term Memory (LSTM) are used. In terms of performance, the ARIMA in this case is better than the LSTM. While the Hybrid ARIMA-LSTM performs better than the single method if we allow more data training included.
AB - The agricultural sector is one of Indonesia's economic strengths. It can be seen from the various kinds of agricultural commodities. As the largest agrarian country in the world, Indonesia can produce agricultural products up to 14.7% - 15% of the total gross domestic product. One of the largest agricultural products in Indonesia is gondorukem (Gum Rosin). Rosin is the sap from this pine tree. Gum rosin exports for 2012 were in response to the increasing international market demand and prices tended to rise from 1,300 USD to 1,500 USD/ton. On January 1, 2019, the price of this commodity became 1,050 USD/ton. It shows that the price of agricultural commodities is very volatile. With this condition, there is concern from both buyer and producer community. There is a difficulty in buying and selling gum rosin. To overcome this, forecasting is applied to predict the estimated price of this commodity. The forecasting model of Autoregressive Integrated Moving Average (ARIMA) and Long-Short Term Memory (LSTM) are used. In terms of performance, the ARIMA in this case is better than the LSTM. While the Hybrid ARIMA-LSTM performs better than the single method if we allow more data training included.
KW - arima
KW - forecasting
KW - gum rosin
KW - long-short term memory
UR - http://www.scopus.com/inward/record.url?scp=85144610696&partnerID=8YFLogxK
U2 - 10.1109/IEIT56384.2022.9967805
DO - 10.1109/IEIT56384.2022.9967805
M3 - Conference contribution
AN - SCOPUS:85144610696
T3 - Proceedings - IEIT 2022: 2022 International Conference on Electrical and Information Technology
SP - 392
EP - 397
BT - Proceedings - IEIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Electrical and Information Technology, IEIT 2022
Y2 - 15 September 2022 through 16 September 2022
ER -