期刊名称：Journal of Statistical Theory and Applications (JSTA)
电子版ISSN：1538-7887
出版年度：2016
卷号：15
期号：4
页码：389-401
DOI：10.2991/jsta.2016.15.4.6
语种：English
出版社：Atlantis Press
摘要：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.
关键词：forecast; exponential smoothing; ARIMA; dynamic linear model; forecast accuracy measure.
