The research group works on statistical and machine learning methods for complex data, with a particular emphasis on time series, spatial and spatio-temporal data, forecasting, nonparametric methods, Bayesian modelling, and interdisciplinary applications.
Our work is often motivated by real-world problems from climate, public health, finance, urban systems, social sciences, policy, and sports. The aim is to develop rigorous methods and apply them to data-rich problems where uncertainty, dependence, heterogeneity, and temporal or spatial structure play an important role.
Key research themes
Click on a theme to view related publications.
Time series and forecasting
Methods for analysing dependent data, forecasting future outcomes, detecting temporal changes, and understanding uncertainty in sequential processes.
View related publications
A nonparametric approach to understand multivariate quantile dynamics in financial time series
arXiv
Pre-printMultiperiod volatility forecasting with optimization-based model selection: Evidence from NIFTY-50 Banks
SSRN
Working paperA survey of statistical and machine learning methods of quantile regression in time series and their suitability in predicting dengue outbreaks
Japanese Journal of Statistics and Data Science, 8(1), 641-689
PublishedtSNE-Spec: A new classification method for multivariate time series data
Journal of Multivariate Analysis, 105537
PublishedImpact of COVID-19 on public social life and mental health: A statistical study of Google Trends data from the USA
Journal of Applied Statistics, 51(3), 581-605
PublishedNew methods of structural break detection and an ensemble approach to analyse exchange rate volatility of Indian rupee during coronavirus pandemic
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(1), 39-61
PublishedForecasting elections from partial information using a Bayesian model for a multinomial sequence of data
Journal of Forecasting, 43(6), 1814-1834
PublishedReal-time forecasting within soccer matches through a Bayesian lens
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), 513-540
PublishedNonparametric quantile regression for time series with replicated observations and its application to climate data
Statistical Science, 39(3), 428-448
PublishedNonparametric method of structural break detection in stochastic time series regression model
arXiv
Pre-printA quadratic trend-based time series method to analyze the early incidence pattern of COVID-19
Biostatistics & Epidemiology, 7(1), e2076529
PublishedAnalyzing airlines stock price volatility during COVID-19 pandemic through internet search data
International Journal of Finance & Economics, 28(2), 1497-1513
PublishedA wavelet-based methodology to compare the impact of pandemic versus Russia-Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets
Energy Economics, 124, 106830
PublishedAn ensemble method for early prediction of dengue outbreak
Journal of the Royal Statistical Society Series A, 185(1), 84-101
Published
Spatio-temporal modelling
Statistical models for data varying over space and time, with applications in air pollution, climate, real estate, disease spread, and urban systems.
View related publications
E-STGCN: Extreme spatiotemporal graph convolutional networks for air quality forecasting
Journal of the Royal Statistical Society Series A: Statistics in Society, qnag010
PublishedNonparametric regression of spatio-temporal data using infinite-dimensional covariates
arXiv
Pre-printA data-driven approach to spatial zoning and anomaly detection in the dynamic real estate network
Environment and Planning B: Urban Analytics and City Science, 23998083251411954
PublishedA divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London
Annals of Operations Research, 1-42
PublishedCPRI-Office: A new commercial property rental index for Indian cities using spatio-temporal modeling techniques
IIMB Working Paper No. 727/2025
Working paperA spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread
AStA Advances in Statistical Analysis, 108(4), 823-851
PublishedNonparametric quantile regression for spatio-temporal processes
arXiv
Pre-printA spatio-temporal statistical model to analyze COVID-19 spread in the USA
Journal of Applied Statistics, 50(11-12), 2310-2329
PublishedA novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
Computational Statistics & Data Analysis, 188, 107810
PublishedA Bayesian approach to identify changepoints in spatio-temporal ordered categorical data: An application to COVID-19 data
arXiv
Under resubmission
Quantile regression and extremes
Quantile-based and tail-focused methods for understanding heterogeneous responses, risks, and extreme behaviour.
View related publications
A nonparametric approach to understand multivariate quantile dynamics in financial time series
arXiv
Pre-printA survey of statistical and machine learning methods of quantile regression in time series and their suitability in predicting dengue outbreaks
Japanese Journal of Statistics and Data Science, 8(1), 641-689
PublishedNonparametric quantile regression for time series with replicated observations and its application to climate data
Statistical Science, 39(3), 428-448
PublishedNonparametric quantile regression for spatio-temporal processes
arXiv
Pre-print
Statistical learning
Machine learning, deep learning, ensemble learning, and statistical learning methods for complex empirical datasets.
View related publications
Optimal selection of the starting lineup for a football team
IIMB Management Review, 100642
PublishedE-STGCN: Extreme spatiotemporal graph convolutional networks for air quality forecasting
Journal of the Royal Statistical Society Series A: Statistics in Society, qnag010
PublishedOptimising football transfer strategy under budget constraints: A weighted multi-criteria approach
arXiv
Pre-printProjection Diagnostics for Directional Asymmetry and Tail-Ratio Departure in Multivariate Data
arXiv
Pre-printWhat elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data
IIMB Management Review, 37(1), 100519
PublishedEnvironmentally Responsible Index Tracking: Maintaining Performance while Reducing Carbon Footprint of the Portfolio
Statistics and Applications, 23(1), 217-223
PublishedtSNE-Spec: A new classification method for multivariate time series data
Journal of Multivariate Analysis, 105537
PublishedA data-driven approach to spatial zoning and anomaly detection in the dynamic real estate network
Environment and Planning B: Urban Analytics and City Science, 23998083251411954
PublishedNew methods of structural break detection and an ensemble approach to analyse exchange rate volatility of Indian rupee during coronavirus pandemic
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(1), 39-61
PublishedAnalyzing airlines stock price volatility during COVID-19 pandemic through internet search data
International Journal of Finance & Economics, 28(2), 1497-1513
PublishedA novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
Computational Statistics & Data Analysis, 188, 107810
PublishedA wavelet-based methodology to compare the impact of pandemic versus Russia-Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets
Energy Economics, 124, 106830
PublishedAn ensemble method for early prediction of dengue outbreak
Journal of the Royal Statistical Society Series A, 185(1), 84-101
PublishedA review and recommendations on variable selection methods in regression models for binary data
arXiv
Under resubmissionA machine learning approach to analyze the effect of situational and player-dependent features on converting freekicks in soccer
Conference Proceedings 2021 Asia-Singapore Conference on Sport Science, p. 19
Published
Sports analytics
Statistical modelling and analytics for football, cricket, tennis, kabaddi, and other sports.
View related publications
Optimal selection of the starting lineup for a football team
IIMB Management Review, 100642
PublishedSkill or chance? A Bayesian analysis of dependence and heterogeneity in penalty shootouts in football
Journal of Sports Analytics, 12
PublishedOptimising football transfer strategy under budget constraints: A weighted multi-criteria approach
arXiv
Pre-printWhat elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data
IIMB Management Review, 37(1), 100519
PublishedReal-time forecasting within soccer matches through a Bayesian lens
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), 513-540
PublishedA goal based index to analyze the competitive balance of a football league
Journal of Quantitative Analysis in Sports, 18(3), 171-186
PublishedA machine learning approach to analyze the effect of situational and player-dependent features on converting freekicks in soccer
Conference Proceedings 2021 Asia-Singapore Conference on Sport Science, p. 19
Published
Environmental research
Applied research using environmental datasets such as air pollution, rainfall, flooding, land surface temperature, and climate data.
View related publications
E-STGCN: Extreme spatiotemporal graph convolutional networks for air quality forecasting
Journal of the Royal Statistical Society Series A: Statistics in Society, qnag010
PublishedEnvironmentally Responsible Index Tracking: Maintaining Performance while Reducing Carbon Footprint of the Portfolio
Statistics and Applications, 23(1), 217-223
PublishedNonparametric quantile regression for time series with replicated observations and its application to climate data
Statistical Science, 39(3), 428-448
Published
Real estate and urban analytics
Statistical methods for large real estate datasets, rental indices, spatial zoning, urban systems, and policy-relevant analytics.
View related publications
A data-driven approach to spatial zoning and anomaly detection in the dynamic real estate network
Environment and Planning B: Urban Analytics and City Science, 23998083251411954
PublishedA divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London
Annals of Operations Research, 1-42
PublishedCPRI-Office: A new commercial property rental index for Indian cities using spatio-temporal modeling techniques
IIMB Working Paper No. 727/2025
Working paper
Other applications
Applied statistical and machine learning research in areas beyond the lab's main thematic clusters, including public health, social systems, policy studies, education, management, and interdisciplinary empirical applications.
View related publications
A survey of statistical and machine learning methods of quantile regression in time series and their suitability in predicting dengue outbreaks
Japanese Journal of Statistics and Data Science, 8(1), 641-689
PublishedEnvironmentally Responsible Index Tracking: Maintaining Performance while Reducing Carbon Footprint of the Portfolio
Statistics and Applications, 23(1), 217-223
PublishedImpact of COVID-19 on public social life and mental health: A statistical study of Google Trends data from the USA
Journal of Applied Statistics, 51(3), 581-605
PublishedNew methods of structural break detection and an ensemble approach to analyse exchange rate volatility of Indian rupee during coronavirus pandemic
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(1), 39-61
PublishedA spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread
AStA Advances in Statistical Analysis, 108(4), 823-851
PublishedForecasting elections from partial information using a Bayesian model for a multinomial sequence of data
Journal of Forecasting, 43(6), 1814-1834
PublishedPrevalence and spectrum of diabetic peripheral neuropathy and its correlation with insulin resistance - An experience from eastern India
International Journal of Advanced Research, 11(06), 1085-1094
PublishedA quadratic trend-based time series method to analyze the early incidence pattern of COVID-19
Biostatistics & Epidemiology, 7(1), e2076529
PublishedAnalyzing airlines stock price volatility during COVID-19 pandemic through internet search data
International Journal of Finance & Economics, 28(2), 1497-1513
PublishedA spatio-temporal statistical model to analyze COVID-19 spread in the USA
Journal of Applied Statistics, 50(11-12), 2310-2329
PublishedA novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
Computational Statistics & Data Analysis, 188, 107810
PublishedA wavelet-based methodology to compare the impact of pandemic versus Russia-Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets
Energy Economics, 124, 106830
PublishedA Bayesian approach to identify changepoints in spatio-temporal ordered categorical data: An application to COVID-19 data
arXiv
Under resubmissionAn ensemble method for early prediction of dengue outbreak
Journal of the Royal Statistical Society Series A, 185(1), 84-101
PublishedPre-prints and working papers
A nonparametric approach to understand multivariate quantile dynamics in financial time series
arXiv
Pre-printMultiperiod volatility forecasting with optimization-based model selection: Evidence from NIFTY-50 Banks
SSRN
Working paperNonparametric regression of spatio-temporal data using infinite-dimensional covariates
arXiv
Pre-printOptimising football transfer strategy under budget constraints: A weighted multi-criteria approach
arXiv
Pre-printProjection Diagnostics for Directional Asymmetry and Tail-Ratio Departure in Multivariate Data
arXiv
Pre-printCPRI-Office: A new commercial property rental index for Indian cities using spatio-temporal modeling techniques
IIMB Working Paper No. 727/2025
Working paperNonparametric quantile regression for spatio-temporal processes
arXiv
Pre-printNonparametric method of structural break detection in stochastic time series regression model
arXiv
Pre-printA Bayesian approach to identify changepoints in spatio-temporal ordered categorical data: An application to COVID-19 data
arXiv
Under resubmissionA review and recommendations on variable selection methods in regression models for binary data
arXiv
Under resubmissionAccepted publications since 2020
Optimal selection of the starting lineup for a football team
IIMB Management Review, 100642
PublishedE-STGCN: Extreme spatiotemporal graph convolutional networks for air quality forecasting
Journal of the Royal Statistical Society Series A: Statistics in Society, qnag010
PublishedSkill or chance? A Bayesian analysis of dependence and heterogeneity in penalty shootouts in football
Journal of Sports Analytics, 12
PublishedWhat elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data
IIMB Management Review, 37(1), 100519
PublishedA survey of statistical and machine learning methods of quantile regression in time series and their suitability in predicting dengue outbreaks
Japanese Journal of Statistics and Data Science, 8(1), 641-689
PublishedEnvironmentally Responsible Index Tracking: Maintaining Performance while Reducing Carbon Footprint of the Portfolio
Statistics and Applications, 23(1), 217-223
PublishedtSNE-Spec: A new classification method for multivariate time series data
Journal of Multivariate Analysis, 105537
PublishedA data-driven approach to spatial zoning and anomaly detection in the dynamic real estate network
Environment and Planning B: Urban Analytics and City Science, 23998083251411954
PublishedA divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London
Annals of Operations Research, 1-42
PublishedImpact of COVID-19 on public social life and mental health: A statistical study of Google Trends data from the USA
Journal of Applied Statistics, 51(3), 581-605
PublishedNew methods of structural break detection and an ensemble approach to analyse exchange rate volatility of Indian rupee during coronavirus pandemic
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(1), 39-61
PublishedA spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread
AStA Advances in Statistical Analysis, 108(4), 823-851
PublishedForecasting elections from partial information using a Bayesian model for a multinomial sequence of data
Journal of Forecasting, 43(6), 1814-1834
PublishedReal-time forecasting within soccer matches through a Bayesian lens
Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), 513-540
PublishedNonparametric quantile regression for time series with replicated observations and its application to climate data
Statistical Science, 39(3), 428-448
PublishedPrevalence and spectrum of diabetic peripheral neuropathy and its correlation with insulin resistance - An experience from eastern India
International Journal of Advanced Research, 11(06), 1085-1094
PublishedA quadratic trend-based time series method to analyze the early incidence pattern of COVID-19
Biostatistics & Epidemiology, 7(1), e2076529
PublishedAnalyzing airlines stock price volatility during COVID-19 pandemic through internet search data
International Journal of Finance & Economics, 28(2), 1497-1513
PublishedA spatio-temporal statistical model to analyze COVID-19 spread in the USA
Journal of Applied Statistics, 50(11-12), 2310-2329
PublishedA novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
Computational Statistics & Data Analysis, 188, 107810
PublishedA wavelet-based methodology to compare the impact of pandemic versus Russia-Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets
Energy Economics, 124, 106830
PublishedA goal based index to analyze the competitive balance of a football league
Journal of Quantitative Analysis in Sports, 18(3), 171-186
PublishedAn ensemble method for early prediction of dengue outbreak
Journal of the Royal Statistical Society Series A, 185(1), 84-101
PublishedA machine learning approach to analyze the effect of situational and player-dependent features on converting freekicks in soccer
Conference Proceedings 2021 Asia-Singapore Conference on Sport Science, p. 19
Published