New approach to forecasting agro-based statistical models
Journal article
Akram, Akram, Bhatti, Ishaq, Ashfaq, Muhammad and Khan, Asif Ali. (2016). New approach to forecasting agro-based statistical models. Journal of Statistical Theory and Applications. 15(4), pp. 387 - 399. https://doi.org/10.2991/Jsta.2016.15.4.6
Authors | Akram, Akram, Bhatti, Ishaq, Ashfaq, Muhammad and Khan, Asif Ali |
---|---|
Abstract | This paper uses various forecasting methods to forecast future crop production levels using time series data for four major crops in Pakistan: wheat, rice, cotton and pulses. These different forecasting methods are then assessed based on their out-of-sample forecast accuracies. We empirically compare three methods: Box- Jenkins’ ARIMA, Dynamic Linear Models (DLM) and exponential smoothing. The best forecasting models are selected from each of the methods by applying them to various agricultural time series in order to demonstrate the usefulness of the models and the differences between them in an actual application. The forecasts obtained from the best selected exponential smoothing models are then compared with those obtained from the best selected classical Box-Jenkins ARIMA models and DLMs using various forecast accuracy measures. |
Keywords | forecast; exponential smoothing; ARIMA; dynamic linear model; forecast accuracy measure |
Year | 2016 |
Journal | Journal of Statistical Theory and Applications |
Journal citation | 15 (4), pp. 387 - 399 |
Publisher | Atlantis Press |
ISSN | 1538-7887 |
Digital Object Identifier (DOI) | https://doi.org/10.2991/Jsta.2016.15.4.6 |
Open access | Open access |
Page range | 387 - 399 |
Research Group | Mary MacKillop Institute for Health Research |
Publisher's version | License |
Place of publication | United States of America |
https://acuresearchbank.acu.edu.au/item/8q11z/new-approach-to-forecasting-agro-based-statistical-models
Download files
59
total views114
total downloads0
views this month2
downloads this month