21 citations · 29 across the 16 of their papers we have counts for
9 papers · 1 filter
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…