21 citations · 29 across the 16 of their papers we have counts for
18 papers
Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data
Donia Besher, Rajdeep Pathak, Madhurima Panja +1
Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across temporal and spatiotemporal fo…
SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-faced Adaptive Traffic Control for Reliable Intelligent Transportation Systems
Pratham Payra, Jagadish B, Jagadish +2
Traffic signal control is a challenging sequential decision-making problem requiring a balance between throughput, vehicle delay fairness, signal stability, predictable switching,…
TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting
Inesh Shukla, Madhurima Panja, Tanujit Chakraborty +1
Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation. However, newly established surveilla…
ANGLE: Angular Neural Generative Learning via Engression
Rajdeep Pathak, Archi Roy, Tanujit Chakraborty
Circular data, representing angles or directions, are frequently encountered in computer vision, biology, geology, and meteorology. Traditional regression targets the conditional m…
Long-Memory Reservoir Computing for Data-Scarce Dengue Forecasting
Rahul Goswami, Shinjini Paul, Palash Ghosh +1
Accurate dengue forecasting is crucial for public health planning, but remains challenging because incidence series are often short, noisy, non-stationary, nonlinear, and often aff…
EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting
Madhurima Panja, Danny D'Agostino, Huitao Li +2
The increasing adoption of data-driven decision-making in public health has established epidemic forecasting as a critical area of research. Recent advances in multivariate forecas…
Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series
Madhurima Panja, Grace Younes, Tanujit Chakraborty
Chaotic time series are notoriously difficult to forecast. Small uncertainties in initial conditions amplify rapidly, while strong nonlinearities and regime dependent variability c…
Deep Generative Transformers for Probabilistic Time Series and Spatiotemporal Forecasting
Rajdeep Pathak, Rahul Goswami, Madhurima Panja +2
Reliable uncertainty quantification is paramount for forecasting multivariate time series and spatiotemporal data. While Transformer architectures excel at sequence modeling, curre…
Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics
Rajdeep Pathak, Tanujit Chakraborty
Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to complex nonlinear temporal dependencie…
Early-Warning Signals of Political Risk in Stablecoin Markets: Human and Algorithmic Behavior Around the 2024 U.S. Election
Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang +4
We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin tra…
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Madhurima Panja, Dhiman Das, Tanujit Chakraborty +6
Understanding and forecasting precipitation events in the Arctic maritime environments, such as Bear Island and Ny-Ålesund, is crucial for assessing climate risk and developing ear…
Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks
Shovon Sengupta, Sunny Kumar Singh, Tanujit Chakraborty
Accurate macroeconomic forecasting has become harder amid geopolitical disruptions, policy reversals, and volatile financial markets. Conventional vector autoregressions (VARs) ove…
Neural ARFIMA model for forecasting BRIC exchange rates with long memory
Donia Besher, Madhurima Panja, Shovon Sengupta +1
Exchange rate forecasting remains a challenging problem, particularly for emerging economies, where the observed time series exhibit pronounced long-memory dependence, nonlinear dy…
Universal Patterns in the Blockchain: Analysis of EOAs and Smart Contracts in ERC20 Token Networks
Kundan Mukhia, SR Luwang, Md. Nurujjaman +3
Scaling laws offer a powerful lens to understand complex transactional behaviors in decentralized systems. This study reveals distinctive statistical signatures in the transactiona…
Probabilistic Forecasting of Climate Policy Uncertainty: The Role of Macro-financial Variables and Google Search Data
Donia Besher, Anirban Sengupta, Tanujit Chakraborty
Accurately forecasting Climate Policy Uncertainty (CPU) is essential for designing climate strategies that balance economic growth with environmental objectives. Elevated CPU level…
Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study
Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty +2
Cholera, an acute diarrheal disease, is a serious concern in developing and underdeveloped areas. A qualitative understanding of cholera epidemics aims to foresee transmission patt…
Pattern change of precipitation extremes in Bear Island
Arnob Ray, Tanujit Chakraborty, Athulya Radhakrishnan +4
Extreme precipitation in the Arctic region plays a crucial role in global weather and climate patterns. Bear Island (Bjørnøya) is located in the Norwegian Svalbard archipelago, whi…
MDAS: A Diagnostic Approach to Assess the Quality of Data Splitting in Machine Learning
Palash Ghosh, Bittu Karmakar, Eklavya Jain +3
In the field of machine learning, model performance is usually assessed by randomly splitting data into training and test sets. Different random splits, however, can yield markedly…