@article{MAKHILLJEAS2019142018547,
    title = {Hybrid of ARIMA and Quantile Regression (ARIMA-QR) Model for
Forecasting Paddy Price in Indonesia},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {20},
    pages = {7609-7619},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.7609.7619},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.7609.7619},
    author = {Wiwik,Faizal,Fajar Ratna,A. and},
    keywords = {Forecasting,ARIMA,quantile regression,ARIMA-QR,paddy prices,fluctuations},
    abstract = {The price of paddy as the main food commodity in Indonesia, from year to year continues to
experience fluctuations but tends to increase over the past few years. This requires that decision makers take
action to maintain price stabilization. Indonesian Bureau of Logistics (BULOG) as a decision maker needs to
know the forecasting of paddy prices over the next periods in order to determine the best actions or policies.
The policy can be in the form of the amount of government paddy reserves, the amount of release of stock to
the market, the determination of the amount of imported paddy and the price of paddy. In this research, the price
of paddy is forecasted by using the ARIMA-QR method to obtain forecasting results for the future period as
well as identifying factors that influence paddy price fluctuations. In doing this forecasting, several variables
are used which influence the fluctuations in paddy prices such as the price of grain basis (GKG) and world
paddy prices, the amount of BULOG stock, Ied holiday and the forecasting value of paddy prices that have
been done previously. The data used is monthly data starting from 2000-2015. Based on the results of the study,
the price forecasting model using ARIMA and ARIMA-QR has an accuracy of 1.47% for q = 0.25,
1.21% for q = 0.5 and 1.42% at the time q = 0.75. This average accuracy is 0.03% lower than the ARIMA
accuracy.}
    }