New approach to Forecasting Agro-based statistical models

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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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  -