THE ELECTION OF THE BEST AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL TO FORECASTING RICE PRODUCTION IN INDONESIA

被引：2

作者：

Tinungki, Georgina Maria ^{[1
]}

机构：

[1] Hasanuddin Univ, Fac Math & Nat Sci, Dept Math, Makassar 90245, Indonesia

来源：

ADVANCES AND APPLICATIONS IN STATISTICS | 2018年 / 52卷 / 04期

关键词：

agriculture; ARIMA model; rice production; time series model;

D O I：

10.17654/AS052040251

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The objective of this research is to identify the best autoregressive integrated moving average (ARIMA) model for forecasting rice production in Indonesia. Rice crops are the most important agricultural commodity in Indonesia, because rice is the staple food of Indonesians. Therefore, the ability to forecast rice production is crucial. We used autoregressive integrated moving average (ARIMA) model to predict rice production in Indonesia. The data analyzed is the data of rice production in Indonesia from 1993 to 2012. The data of 2013 to 2015 got used to validate the forecasting results. The results show that the ARIMA model (2, 2, 0) is the best model for forecasting rice production in Indonesia.

引用

页码：251 / 265

页数：15

共 50 条

[21] Forecasting House Price Index for USA with Autoregressive Integrated Moving Average Models
Shi Jiangang
Zou Wei
ADVANCES IN MANAGEMENT OF TECHNOLOGY, PT 1, 2009, : 590 - 594
[22] A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
Warut Pannakkong
Van-Nam Huynh
Songsak Sriboonchitta
International Journal of Computational Intelligence Systems, 2019, 12 : 1047 - 1061
[23] Forecasting the Price of Indonesia's Rice Using Hybrid Artificial Neural Network and Autoregressive Integrated Moving Average (Hybrid NNs-ARIMAX) with Exogenous Variables
Anggraeni, Wiwik
Mahananto, Faizal
Sari, Ayusha Qamara
Zaini, Zulkifli
Andri, Kuntoro Boga
Sumaryanto
FIFTH INFORMATION SYSTEMS INTERNATIONAL CONFERENCE, 2019, 161 : 677 - 686
[24] A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
Pannakkong, Warut
Van-Nam Huynh
Sriboonchitta, Songsak
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2019, 12 (02) : 1047 - 1061
[25] Forecasting Road Traffic Fatalities in Malaysia Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model
Sim, Ho Jen
Chong, Choo Wei
Abu Kassim, Khairil Anwar
Mooi, Ching Siew
Zhang Yuruixian
PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY, 2022, 30 (02): : 897 - 911
[26] Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model
Zhang, Xujun
Pang, Yuanyuan
Cui, Mengjing
Stallones, Lorann
Xiang, Huiyun
ANNALS OF EPIDEMIOLOGY, 2015, 25 (02) : 101 - 106
[27] Wind speed forecasting using autoregressive moving average/generalized autoregressive conditional heteroscedasticity model
Jiang, Wen
Yan, Zheng
Feng, Dong-Han
Hu, Zhi
EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 2012, 22 (05): : 662 - 673
[28] Long Term Electricity Demand Forecasting Using Autoregressive Integrated Moving Average Model: Case Study of Morocco
Citroen, Noreddine
Ouassaid, Mohammed
Maaroufi, Mohamed
PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON ELECTRICAL AND INFORMATION TECHNOLOGIES (ICEIT 2015), 2015, : 59 - 64
[29] Hotspots Forecasting Using Autoregressive Integrated Moving Average (ARIMA) for Detecting Forest Fires
Slavia, Athaya Putri
Sutoyo, Edi
Witarsyah, Deden
2019 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND INTELLIGENCE SYSTEM (IOTAIS), 2019, : 92 - 97
[30] Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model
Kissi, Ernest
Adjei-Kumi, Theophilus
Amoah, Peter
Gyimah, Jerry
CONSTRUCTION ECONOMICS AND BUILDING, 2018, 18 (01): : 70 - 82

← 1 2 3 4 5 →