Important Note : 
1. Please click on the [Paper | Code] to access the paper and code. Also, please refer to my Google Scholar Profile and RGate Profile
2. Classifications of Publications: Journal Publication | Conference Publication | ​Book Chapter | Best Paper Award
3. * : Both authors contributed equally;  ^ : A student/doctoral fellow/mentee of Dr. Tanujit Chakraborty during the time of the work.

​Year-wise Publications :

2023

• Chakraborty, T., Panja, M., Kumar, U., & Hadid, A. (2023). Probabilistic Autoregressive Neural Network Model for Time Series Forecasting. Under Review. ​[Paper | Code]
• Panja, M*., Chakraborty, T.*, Kumar, U., & Liu, N. (2023). Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemic. Under Review. ​[Paper | Code]
• Chakraborty, T., Kamat, G.^, Chakraborty, A. K. (2023). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Accepted. [Paper | Code] 
• Panja, M*., Chakraborty, T.*, Nadim. Sk., Ghosh. I., Kumar, U., & Liu, N. (2023). An ensemble neural network approach to forecast Dengue outbreak based on climatic condition. Chaos, Solitons & Fractals, Vol. 167, pg. 1-14. [Paper | Code]

2022

• Chakraborty, T., Chattopadhyay, S,, Das, S., Kumar, U., Jayavelu, S. (2022). Searching for heavy-tailed probability distributions for modeling real-world complex networks. IEEE Access, Vol. 10, 115092-115107. ​[Paper | Code]  
• Chakraborty, T., Das, S., & Chattopadhyay, S. (2022). A New Method for Generalizing Burr and Related Distributions. Mathematica Slovaca, Vol. 72, pg. 241-264. [Paper | Code]
• Bhattacharyya, A*., Chakraborty, T.*, & Rai, S. N. (2022). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, Vol. 107, pg. 3025-3040. ​[Paper | Code]
• ​​​​Sasal, L.^, Chakraborty, T., & Hadid, A. (2022). W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting. In International Conference on Machine Learning and Applications.​ [Paper | Code]
• Elabid, Z.^, Chakraborty, T., & Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. In International Conference on Machine Learning and Applications.​ [Paper | Code]

• Chakraborty, T., Ghosh, I., Mahajan, T.^, Arora, T.^ (2022). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modeling, Control and Drug Development for COVID-19 Outbreak Prevention, Vol. 366, pg. 1023-1064, Springer Book Chapter, Cham. ​​​[Paper | Code]
• Panja, M.^, Chakraborty, T., Kumar, U. (2022). Data Dictionary. In Encyclopedia of Mathematical Geosciences, Springer Book Chapter, Cham. ​​​[Link]
• Chakraborty, T., Kumar, U. (2022). Loss Functions. In Encyclopedia of Mathematical Geosciences, Springer Book Chapter, Cham. ​​​[Link]

​2021

• Ray, A.*, Chakraborty, T.*, & Ghosh, D. (2021). Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events​. Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 31, pg. 1-15.​ [Paper | Code]
• Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Modified Lomax Model: A heavy-tailed distribution for fitting large-scale real-world complex networks.  Social Network Analysis and Mining, Vol. 11, pg. 11-43. [Paper]
• Ghosh, I., Chakraborty, T. (2021). An integrated deterministic-stochastic approach for forecasting the long-term trajectories of COVID-19. International Journal of Modeling, Simulation, and Scientific Computing, Vol. 12, pg. 1-15.​ [Paper]
• Chakraborty, T., Chakraborty, A. K., Biswas, M., Banerjee, S.^, & Bhattacharya, S.^ (2021). Unemployment Rate Forecasting: A Hybrid Approach. Computational Economics, Vol. 57, pg. 183-201. [Paper] 
• Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., & ​Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. In IEEE International Joint Conference on Neural Networks (IJCNN).​ [Paper]
•  Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks : A journey beyond scale-free.  In ACM IKDD CODS-COMAD. (Best Paper Award Winner at ACM CODS-COMAD) [Paper] ​

​
2020

• Chakraborty, T., Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals, Vol. 135, pg. 1-10. [Paper | Code]
• Chakraborty, T., Chakraborty, A. K. (2020). Hellinger Net : A Hybrid Imbalance Learning Model to Improve Software Defect Prediction. IEEE Transactions on Reliability, Vol. 70, pg. 481-494. [Paper]
• Chakraborty, T., & Chattopadhyay, S., & Chakraborty, A. K. (2020). Radial basis neural tree model for improving waste recovery process in a paper industry. Applied Stochastic Models in Business and Industry, Vol. 36, pg. 49-61. [Paper]
• Chakraborty, T., Chakraborty, A. K. (2020). Superensemble classifier for improving predictions in imbalanced data sets. Communications in Statistics - Case Studies and Data Analysis, Vol. 6, pg. 123-141. [Paper]

2019

• Chakraborty, T., Chattopadhyay, S., & Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, Vol. 527, pg. 1-8. [Paper]
• Chakraborty, T., Chakraborty, A. K., & Chattopadhyay, S. (2019). A novel distribution-free hybrid regression model for manufacturing process efficiency improvement. Journal of Computational and Applied Mathematics​, Vol. 362, pg. 130-142. [Paper] (Best Student Paper Award Winner at 52nd Annual Convention of ORSI, India) 
• Chakraborty, T., Chakraborty, A. K., & Mansoor, Z.^ (2019). A hybrid regression model for water quality prediction. OPSEARCH, Vol. 56, pg. 1167-1178. [Paper]
• ​Chakraborty, T., Chakraborty, A. K., & Murthy, C. A. (2019). A nonparametric ensemble binary classifier and its statistical properties. Statistics & Probability Letters, Vol. 149, pg. 16-23. ​[Paper]

​​
2018

• Chakraborty, T., Chattopadhyay, S., & Chakraborty, A. K. (2018). A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. OPSEARCH, Vol. 55, pg. 434-446. [Paper] (B.G. Raghavendra Memorial Award Winner from ORSI, India)​