Journal of Statistical Theory and Applications

Volume 15, Issue 4, December 2016, Pages 387 - 399

New approach to Forecasting Agro-based statistical models

Authors
Muhammad Akram, M. Ishaq Bhatti, Muhammad Ashfaq, Asif Ali Khan
Corresponding Author
Muhammad Akram
Received 15 May 2015, Accepted 28 June 2016, Available Online 1 December 2016.
DOI
10.2991/jsta.2016.15.4.6How to use a DOI?
Keywords
forecast; exponential smoothing; ARIMA; dynamic linear model; forecast accuracy measure.
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.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Statistical Theory and Applications
Volume-Issue
15 - 4
Pages
387 - 399
Publication Date
2016/12/01
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
10.2991/jsta.2016.15.4.6How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Muhammad Akram
AU  - M. Ishaq Bhatti
AU  - Muhammad Ashfaq
AU  - Asif Ali Khan
PY  - 2016
DA  - 2016/12/01
TI  - New approach to Forecasting Agro-based statistical models
JO  - Journal of Statistical Theory and Applications
SP  - 387
EP  - 399
VL  - 15
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2016.15.4.6
DO  - 10.2991/jsta.2016.15.4.6
ID  - Akram2016
ER  -