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ThesisItem Open Access Volatility forecast models of prices and arrivals of tomato in APMC markets of Haryana(CCSHAU, Hisar, 2023-01) Pushpa; JoginderThe majority of agricultural time series data are nonlinear, nonstationary and leptokurtic in nature. Thus, one of the most difficult areas of time series forecasting is agricultural price forecasting. Accurate forecasting assists both farmers and policymakers in making good decisions. According to the literature, each of the forecasting models has its own set of limitations. In the current study, forecasting performance of SARIMA, GARCH, ANN, Hybrid (SARIMA-GARCH and SARIMA-ANN) and multivariate time series (VAR and VARMA) models has been compared for monthly prices and arrivals of tomato in selected markets of Haryana. The purpose of the study is to give short term forecast of prices and arrivals of tomato with various forecast horizons such as one, three, six, nine and twelve months. Based on empirical results of the study, it is found that ANN models outperformed the others models for all horizon except one month ahead based on performance measures like MAPE and SEP. It is observed that Hybrid (SARIAM-ANN) models do not enhance the forecasting performance. The hybrid (SARIMA-GARCH) model outperforms the individual SARIMA and GARCH models in forecasting the prices and arrivals of tomato. It can be seen that the residuals obtained from linear SARIMA models contain the appropriate ARCH effect. The results of multivariate time series reveal that VARMA model outperforms the VAR model based on minimum values of forecasting performance measures such as MAPE and SEP.ThesisItem Open Access Comparative study of ARIMA and ARIMAX models for sugarcane yield forecasting in Northern Agro-climate zone of Haryana(CCSHAU, 2019) Pushpa; Verma, UrmilCrop yield. forecasting is. one of the most important aspects of agricultural statistics system. An efficient crop forecasting is pre-requisite for information system about food supply, especially export–import policies, procurement and price-fixation. The study has been performed in two parts i.e. the development of ARIMA and ARIMAX models for sugarcane yield prediction in Karnal, Ambala and Kurukshetra districts of Haryana. The ARIMA models have been fitted using sugarcane yield data for the period 1966-67 to 2011-12 of Karnal and Ambala districts and 1972-73 to 2011-12 of Kurukshetra district. However, the fortnightly weather data from 1978-79 to 2011-12 have been utilized as input for ARIMAX model building. The validity of fitted models have been checked for subsequent years i.e. 2012-13 to 2016-17, not included in the development of the models. ARIMA(0,1,1) for Karnal, Ambala and Kurukshetra district have been fitted for sugarcane yield forecasting. Secondly, the ARIMA models with alternative combinations of weather variables were tried for fitting ARIMAX models. Consequently, ARIMA(0,1,1) for Karnal, Ambala and Kurukshetra districts along with fortnightly weather variables (viz., tmx7, tmn1, tmn10, arf8, arf11 over the crop growth period) as input series were utilized in fitting ARIMAX models. ARIMA and ARIMAX models both provided the relationships to reliably estimate the sugarcane yield. The predictive performance(s) of the contending models were observed in terms of the percent deviations of sugarcane yield forecasts in relation to the observed yield(s) and root mean square error(s). The level of accuracy achieved by ARIMA model(s) with weather as input was considered adequate for predicting sugarcane yield(s) i.e. the ARIMAX models consistently showed superiority over ARIMA models in capturing lower percent deviations pertaining to sugarcane yield estimates. The ARIMAX models performed well with lower error metrics as compared to ARIMA models in all time regimes. Five-steps ahead estimated values of sugarcane yield(s) favour the use of ARIMAX models to get pre-harvest sugarcane yield prediction in the districts under consideration.
