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Development of Hybrid Time Series Model using Machine Learning Techniques for Forecasting Crop Yield with Covariates

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Title Development of Hybrid Time Series Model using Machine Learning Techniques for Forecasting Crop Yield with Covariates
Not Available
 
Creator MD. Wasi Alam
Kanchan Sinha
Rajeev Kumar Ranjan
Mrinmoy Roy
Santosha Rathod
Kamlesh Narayan Singh
 
Subject ARIMAX Model
Univariate Linear Time Series
Hybrid Linear Time Series
 
Description Not Available
Meeting food demand of the growing population is a major challenge. In order to ensure food
security of this mega country in upcoming couple of decades, a precise Government policy is
required. Although, some of the institutes of ICAR have formulated their vision 2030 or
2050, but, the projection of the food demand and supply mentioned in the vision are not
based on sound statistical foundation. Enhancing the accuracy and reliability of the forecast
of the food production for upcoming couple of decades is a major challenge. Production is
multiplication of cropped area with yield. In this study, we are focused on yield as we have
considered time series weather variables for forecasting yield. Although, few work on short
term time series forecast of the crop yield are available in literature but hardly any work is
available on long term forecast of crop yield using hybrid time series model using weather
variables as covariates. In an attempt to get long term forecast of the crop production, we
have used Box Jenkins linear time series approach in first instance and obtained the residuals
and improved the forecast values of yield by hybrid linear time series approach using
machine learning techniques like artificial neural network and support vector machine. We
have also proposed a technique to compute the long term forecast using the improved short
term forecast through the hybrid approaches. The improved short term forecast values of
yield have been considered as the baseline data and through the proposed approach we get
the long term forecast of yield up to desired forecast horizon. Apart from this, autoregressive
integrated moving average using exogenous variable (ARIMAX) model has also been used
for yield forecast using weather variables. The forecasted yield through ARIMAX model has
been further improved through hybrid approaches and has been considered as baseline data
for further long term yield forecast up to the desired year.
Not Available
 
Date 2018-08-16T10:37:25Z
2018-08-16T10:37:25Z
2018-01-01
 
Type Project Report
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/6472
 
Language English
 
Relation Not Available;
 
Publisher ICAR-IASRI, New Delhi