Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction
Because of global climate change, lack of arable land, and rapid population growth, the supplies of three major food crops (i.e., rice, wheat, and corn) have been gradually decreasing worldwide. The rapid increase in demand for food has contributed to a continuous rise in food prices, which directly...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1910520 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563824815767552 |
|---|---|
| author | Yuehjen E. Shao Jun-Ting Dai |
| author_facet | Yuehjen E. Shao Jun-Ting Dai |
| author_sort | Yuehjen E. Shao |
| collection | DOAJ |
| description | Because of global climate change, lack of arable land, and rapid population growth, the supplies of three major food crops (i.e., rice, wheat, and corn) have been gradually decreasing worldwide. The rapid increase in demand for food has contributed to a continuous rise in food prices, which directly threatens the lives of over 800 million people around the world who are reported to be chronically undernourished. Consequently, food crop price prediction has attracted considerable attention in recent years. Recent integrated forecasting models have developed various feature selection methods (FSMs) to capture fewer, but more important, explanatory variables. However, one major problem is that the future values of these important explanatory variables are not available. Thus, predictions based on these variables are not actually possible. Because an autoregressive integrated moving average (ARIMA) can extract important self-predictor variables with future values that can be calculated, this study incorporates an ARIMA as the FSM for computational intelligence (CI) models to predict three major food crop (i.e., rice, wheat, and corn) prices. Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). The predictive accuracies of ARIMA, ANN, SVR, MARS, and the proposed integrated model are compared and discussed. Experimental results reveal that the proposed integrated model achieves superior forecasting performance for predicting food crop prices. |
| format | Article |
| id | doaj-art-0f19ab50425c413aa4700c812167cf01 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0f19ab50425c413aa4700c812167cf012025-02-03T01:12:23ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/19105201910520Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price PredictionYuehjen E. Shao0Jun-Ting Dai1Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, TaiwanDepartment of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, TaiwanBecause of global climate change, lack of arable land, and rapid population growth, the supplies of three major food crops (i.e., rice, wheat, and corn) have been gradually decreasing worldwide. The rapid increase in demand for food has contributed to a continuous rise in food prices, which directly threatens the lives of over 800 million people around the world who are reported to be chronically undernourished. Consequently, food crop price prediction has attracted considerable attention in recent years. Recent integrated forecasting models have developed various feature selection methods (FSMs) to capture fewer, but more important, explanatory variables. However, one major problem is that the future values of these important explanatory variables are not available. Thus, predictions based on these variables are not actually possible. Because an autoregressive integrated moving average (ARIMA) can extract important self-predictor variables with future values that can be calculated, this study incorporates an ARIMA as the FSM for computational intelligence (CI) models to predict three major food crop (i.e., rice, wheat, and corn) prices. Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). The predictive accuracies of ARIMA, ANN, SVR, MARS, and the proposed integrated model are compared and discussed. Experimental results reveal that the proposed integrated model achieves superior forecasting performance for predicting food crop prices.http://dx.doi.org/10.1155/2018/1910520 |
| spellingShingle | Yuehjen E. Shao Jun-Ting Dai Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction Complexity |
| title | Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction |
| title_full | Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction |
| title_fullStr | Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction |
| title_full_unstemmed | Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction |
| title_short | Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction |
| title_sort | integrated feature selection of arima with computational intelligence approaches for food crop price prediction |
| url | http://dx.doi.org/10.1155/2018/1910520 |
| work_keys_str_mv | AT yuehjeneshao integratedfeatureselectionofarimawithcomputationalintelligenceapproachesforfoodcroppriceprediction AT juntingdai integratedfeatureselectionofarimawithcomputationalintelligenceapproachesforfoodcroppriceprediction |