B. HARI MALLIKARJUNA REDDY AND B. SAROJAMMA*

Department of Statistics, Sri Venkateswara University, Tirupati – 517 502.

ABSTRACT

In Time series and forecasting, there are wide range of models are there for fitting and prediction. They are regression
models like Simple Regression models, Multiple Regression models, Polynomial Regression models, Logistic Regression models,
Exponential models etc. Moving average models, Auto regression models, Vector Auto regression models (VAR), Generalised
Autoregressive conditional Heteroscadesticity (GARCH), Auto Regressive Conditional Heteroscadesticity (ARCH) etc. In this
paper, we are used Exponential Smoothing models like Simple Exponential Smoothing model, Holt linear trend model, Brow’s
linear trend model and Damped trend models for Agriculture data like Rice, wheat, Nutri Cereals, Pulses, Oilseeds, Sugar cane,
cotton and other Food grains of ten years data from 2008 to 2017. By using Root Mean Square Error criteria, We estimate best
models for Agriculture data among four Exponential Smoothing models.

KEYWORDS:

Agriculture data, exponential smoothing models, root mean square error criteria.