5 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…
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…
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…
Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators
Rajdeep Pathak, Amit Basak, Sayantee Jana
The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance,…
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…