ARIMAmodel for forecasting of greengram prices in Telangana by using SAS
| Autor: | Panasa Venkatesh, A. Sreenivas, R. Vijaya Kumari, G. Ramakrishna |
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| Rok vydání: | 2019 |
| Předmět: |
Identification (information)
Statistics::Applications Series (mathematics) Bayesian information criterion Autocorrelation Statistics Statistics::Methodology Autoregressive integrated moving average Akaike information criterion Partial autocorrelation function Physics::Atmospheric and Oceanic Physics Statistics::Computation Mathematics |
| Zdroj: | INTERNATIONAL RESEARCH JOURNAL OF AGRICULTURAL ECONOMICS AND STATISTICS. 10:210-214 |
| ISSN: | 2231-6434 2229-7278 |
| Popis: | Autoregressive integrated moving average (ARIMA) approach has been applied for modeling and forecasting of greengram prices in Telangana. Autocorrelation (AC) and partial autocorrelation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. To this end, evaluation of forecasting is carried out with Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion ( BIC). The best identified model for the data under consideration was used for out-of-sample forecasting upto November 2019. |
| Databáze: | OpenAIRE |
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