Publications (Year-wise): 
​
1. Please refer to my Google Scholar Profile | RGate Profile, and GitHub repository for recent updates
2. Classifications of Publications: Journal Publication | Conference Publication | Case Studies | ​Book Chapter 
3. * Indicates Joint First Authors

Journal Publications: 
  • Liu, X., Deliu, N., Chakraborty, T., Bell, L., Chakraborty, B. (2024+) Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. Under Minor Revision in Annals of Applied Statistics. [Paper | Code] 
  • Naik, S.M.*, Chakraborty, T.*, Hadid, A., Chakraborty, B. (2024+) Skew Probabilistic Neural Networks for Learning from Imbalanced Data. Under Major Revision in Pattern Recognition. [Paper | Code]​​
  • Ray, A.*, Chakraborty, T.*, Radhakrishnan, A., Hens, C., Dana, S. K., Ghosh, D., Murukesh, N. (2024+) Pattern change of precipitation extremes in Bear Island. Under Major Revision in Scientific Reports. [Paper] ​
  • Borah, J.*, Chakraborty, T.*, Nadzir, M., Cayetano, M., Benedetto, F. Majumdar, S. (2024+) WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data. Under Minor Revision in IEEE Sensor Letters.  [Paper | Code]
  • Sengupta, S.*, Chakraborty, T.*, Singh, S.K. (2024) Forecasting CPI inflation under economic policy and geo-political uncertainty. International Journal of Forecasting (In Press). [Paper | Code]
  • Chakraborty, T., Reddy U., Naik, S.M., Panja, M., Manvitha, B. (2024). Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art. Machine Learning: Science and Technology, Vol. 5, pg. 1-35. [Paper] 
  • Hadid, A.*, Chakraborty, T.*, Busby, D. (2024) When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges. Expert Systems. Vol. 41, pg. 1-16. [Paper]
  • ​​​Panja, M*., Chakraborty, T.*, Kumar, U., Liu, N. (2023). Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemic. Neural Networks, Vol. 165, pg. 185-212.  ​[Paper | Code | Medium Post | R Package]
  • Chakraborty, T., Kamat, G., Chakraborty, A. K. (2023). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Vol. 65, pg. 1-26. [Paper | Code] ​
  • Thottolil, R., Kumar, U., Chakraborty, T. (2023). Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model. Scientific Reports, Vol. 13, pg. 1-18. [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]
  • 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]
  • 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]
  • 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] 
  • 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 | Code]
  • 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]
  • 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] ​
  • 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 | Code]​

Conference Publications: 
  • Dey, M., Chakrabartty, A., Sarkar, D., Chakraborty, T. (2024). Do we really need Foundation Models for multi-step-ahead Epidemic Forecasting? NeurIPS - TSALM Workshop. ​[Paper]
  • Sadhukhan, P., Sengupta, K., Palit, S., Chakraborty, T. (2024). Knowing the class distinguishing abilities of the features, to build better decision-making models. AMCIS. [Paper]
  • Sadhukhan, P., Chakraborty, T., Sengupta, K. (2024). Deploying model obfuscation: towards the privacy of decision-making models on shared platforms. AMCIS. [Paper]
  • Panja, M., Chakraborty, T., Kumar, U., Hadid, A. (2023). Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting. ICONIP. ​[Paper | Code]
  • Dutta, A., Panja, M., Kumar, U., Hens, C., Chakraborty, T. (2023). Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events. NeurIPS - AI4Science. ​[Paper | Code]
  • Sasal, L., Chakraborty, T., Hadid, A. (2022). W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting. IEEE ICMLA.​ [Paper | Code]
  • Elabid, Z., Chakraborty, T., Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. In IEEE ICMLA.​ [Paper | Code]
  • Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., ​Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. IEEE IJCNN [Paper | Code]
  • Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks : A journey beyond scale-free. ACM IKDD. [Paper] ​ 
  • Chowdhury, P., Chakraborty, T. (2020). Multiplicative Error Modeling Approach for Time Series Forecasting.  IEEE ICCCS. [Paper]
  • Trivedi, K., Chakraborty, T. (2019). A Hybrid Binary Classifier for Pattern Classification. IEEE ICDSE. [Paper]

​Case Studies:
  • Chakraborty, T. (2019). Imbalanced Ensemble Classifier for Learning from Imbalanced Business School Dataset. International Journal of Mathematical, Engineering and Management Sciences, 4(4), 861-869. [Paper]
  • 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., 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]

Book Chapters:
  • 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. ​​​[Paper | Code]
  •  Chakraborty, T., Kumar, U. (2022).​ Loss Function. In Encyclopedia of Mathematical Geosciences, Springer. ​​​[Paper | Code]
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