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/).
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 -