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
AuthorsAkram, 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.

Keywordsforecast; exponential smoothing; ARIMA; dynamic linear model; forecast accuracy measure
Year2016
JournalJournal of Statistical Theory and Applications
Journal citation15 (4), pp. 387 - 399
PublisherAtlantis Press
ISSN1538-7887
Digital Object Identifier (DOI)https://doi.org/10.2991/Jsta.2016.15.4.6
Open accessOpen access
Page range387 - 399
Research GroupMary MacKillop Institute for Health Research
Publisher's version
License
Place of publicationUnited States of America
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https://acuresearchbank.acu.edu.au/item/8q11z/new-approach-to-forecasting-agro-based-statistical-models

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