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New approach to forecasting agro-based statistical models
Akram, Akram Bhatti, Ishaq Ashfaq, Muhammad Khan, Asif Ali
Akram, Akram
Bhatti, Ishaq
Ashfaq, Muhammad
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
Date
2016
Type
Journal article
Journal
Journal of Statistical Theory and Applications
Book
Volume
15
Issue
4
Page Range
387-399
Article Number
ACU Department
Collections
Relation URI
Source URL
Event URL
Open Access Status
Open access
License
CC BY 4.0
File Access
Open