Dr. Mrinmoy Ray


Dr. Mrinmoy Ray

  1. Nonparametric bootstrap approach for constructing prediction intervals for non-linear and bivariate time series models (PI).
  2. Development of Hybrid Time Series Models using Machine Learning Techniques for Forecasting Crop Yield with Covariates (Co-PI).
  3. Forecasting of spatio-temporal time series data using Space Time Autoregressive Moving Average (STARMA) model (Co-PI).
  4. Future perspective of Bt. technology in Indian agriculture (PI).




  1. Junior Research Fellowship by ICAR
  2. University Merit Scholarship by Uttar BangaKrishiViswaVidyalaya, Coochbehar, West Bengal during the period 2005 to 2009
  1. Ray, M., Rai, A., Singh, K. N., V., Ramasubramanian and Kumar, A. (2017). Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India. Technological Forecasting and Social Change118, 128-133.
  2. Anjoy, P., Paul, R. K., Sinha, K., Paul, A. K. and Ray, M. (2017). A hybrid wavelet based neural networks model for predicting monthly WPI of pulses in India. Indian Journal of Agricultural Sciences87 (6), 834-839.
  3. Kanchan, S., Gurung, B., Paul, R.K., Kumar, A., Panwar, S., Alam, W., Ray, Mand Rathod, S. (2017). Volatility Spill over using multivariate GARCH model: An application in futures and spot market price of black pepper. Journal of the Indian Society of Agricultural Statistics71 (1), 21-28.
  4. Rathod, S., Singh, K.N., Paul, R.K., Meher, S.K., Mishra, G.C., Gurung, B., Ray, M. and Sinha, K. (2017). An improved ARFIMA model using maximum overlap discrete wavelet transform (MODWT) and ANN for forecasting agricultural commodity price. Journal of the Indian Society of Agricultural Statistics71(2), 103-111.
  5. Ray, M., Rai, A, V., Ramasubramanian and Singh K. N. (2016). ARIMA-WNN hybrid model for forecasting wheat yield time series data. Journal of the Indian Society of Agricultural Statistics70(1), 63-70.
  6. Ray, M., V., Ramasubramanian, Kumar, A. and Rai, A. (2014). Application of time series intervention modelling for modelling and forecasting cotton yield. Statistics and Applications12 (1&2), 61-70.
  7. Rathod, S., Singh, K.N., Arya, P., Ray, M., Mukherjee, A., Sinha, K., Kumar, P. and Shekhawat, R. S. (2017). Forecasting maize yield using ARIMA-Genetic Algorithm approach. Outlook on Agriculture46 (4), 265-271.
  8. Ray, M., Rai, A., Singh, K. N. and V., Ramasubramanian. (2017).Modeling and forecasting of hybrid rice yield using a grey model improved by the genetic algorithm. International Journal of Agricultural and Statistical Sciences13 (2), 563-566.
  9. Rathod, S., Singh, K.N., Patil, S.G., Naik, R.H., Ray, M., and Meena, V. (2018). Modeling and forecasting of oilseed production of India through artificial intelligence techniques. Indian Journal of Agricultural Science88 (1), 22-27.

Books Edited (e-books):

  1. Advances in Statistical Modeling and Forecasting in Agriculture by Bishal Gurung and Mrinmoy Ray, ICAR- IASRI, New Delhi. http://cbp.icar.gov.in/ebook22.aspx?trainingApprovedId=CAFT-20161529&trainingTitle=Advances%20in%20Statistical%20Modeling%20and%20Forecasting%20in%20Agriculture


Book Chapters:

  1. Mukherjee, A., Rakshit, S., Nag, A., Ray, M., Kharbikar, H. L., Kumari, S., Sarkar, S., Paul, S., Roy, S., Maity, A., Meena, V. S. and Burman, R. R. (2016). Climate Change Risk Perception, Adaptation and Mitigation Strategy: An Extension Outlook in Mountain Himalaya. In: Jaideep Kumar Bisht, Vijay Singh Meena, Pankaj Kumar Mishra and Arunava Pattanayak Edition. Conservation Agriculture (pp. 257-292). Singapore. Springer Singapore.
Skip to toolbar