Journal of Food, Agriculture and Environment

The challenge of selecting the best forecasting model for a time series data


Bhola Nath, D. S. Dhakre, K. A. Sarkar, D. Bhattacharya

Recieved Date: 2020-01-30, Accepted Date: 2020-03-25


Fitting of an appropriate model to an observed time series data for the purpose of efficient prediction is always a challenging task to the researchers. The practitioners of statistics in their first attempt always try to fit a parametric regression model to the data observed. For all parametric models to be fitted, it is assumed that the model errors should follow independent normal distribution strictly or their distribution should be known. If that assumption on error distribution is not satisfied sometimes, then we should search for an alternative procedure of modelling such type of data. Here, we propose the nonparametric regression procedure as the alternative approach and try to study its performance. In this investigation the secondary data on production of rice for the Kharif season and production of wheat for Rabi season for India as a whole for 51 years (1962-1963 to 2012-2013) have been used. It has been found that the variable, production of rice, does not hold the assumption of normal distribution of errors but the variable, production of wheat satisfies this assumption of normality of error distribution. Here we have applied parametric and nonparametric regression and spline regression approaches to both the data sets. It has been observed that there is a great decrease in the value of Mean Absolute Percentage Error (MAPE) of the prediction for the dependent variable, i.e., production of rice when nonparametric regression is used. It is concluded that the nonparametric regression works pretty well for the data set for which the normality assumption of the error distribution does not satisfy and gives better prediction than traditional parametric regression. If data set contain a large number of observations, then spline regression fits the data well.


Assumptions, exponential fitting, MAPE, nonparametric regression, normal distribution, parametric regression, spline regression

Journal: Journal of Food, Agriculture and Environment
Year: 2020
Volume: 18
Issue: 2
Category: Environment
Pages: 97-102

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