DESCRIPTION OF COURSES
AS 667 FORECASTING TECHNIQUES (1L+1P)
The students would be exposed to concepts of forecasting techniques. This course prepares students for undertaking research in this area. This also helps prepare students for applications of this important subject to agricultural sciences.
Forecasting techniques with special reference to agriculture. Forecast based on time series data: exponential smoothing, Box - Jenkins approach and non-linear models. Forecast models using weather parameters, crop-weather relationships and their use in yield forecast. Forecast using plant characters.
Forecast surveys, between-year models (regression model, Markov chain probability model and group method of data handling) and within-year models. Agro-meteorological models: climatic water balance model and crop yield assessment. Forewarning of crop pests and diseases. Application of remote sensing techniques in forecasting. Use of ANN in forecasting.
Fitting of forecast models using weather parameters. Time series analysis: plots, decomposition, stationarity tests, exponential smoothing. Univariate Box - Jenkins ARIMA models and seasonal ARIMA models. Forecast models using plant characters, Agrometeorological models for crop forecasting, Markov chain models and ANN models.