1Department of Mathematics and Statistics, CCS Haryana Agricultural University, Hisar-125 004, Haryana, India
2University College, Kurukshetra University, Kurukshetra-136 119, Haryana, India
*Corresponding author: Email: vermas2l@hotmail.com
Online published on 25 January, 2016.
Autoregressive Integrated Moving Average (ARIMA) models are widely used for analyzing the time-series data. In this approach, the underlying parameters are assumed to be constant but in reality this assumption is rarely met. In agriculture, the data that are generally collected over time will have time-dependency in parameters. Therefore, such data can be analyzed using state space modeling by the application of Kalman filtering technique. In the present study, two procedures were compared for sugarcane yield modeling and forecasting in Haryana, India, to highlight the advantage of using state space procedure pertaining to model data from Indian agriculture.
Autoregressive, moving average, Kalman filtering technique, state space modeling, sugarcane yield forecast
