@inproceedings{chandorkar2019dynamic,title={Dynamic Time Lag Regression: Predicting What \& When},author={Chandorkar, Mandar and Furtlehner, Cyril and Poduval, Bala and Camporeale, Enrico and Sebag, Michele},booktitle={International Conference on Learning Representations},year={2020},}
AGU
Bayesian Inference of Quasi-Linear Radial Diffusion Parameters using Van Allen Probes
@article{sarma2020bayesian,title={Bayesian Inference of Quasi-Linear Radial Diffusion Parameters using Van Allen Probes},author={Sarma, Rakesh and Chandorkar, Mandar and Zhelavskaya, Irina and Shprits, Yuri and Drozdov, Alexander and Camporeale, Enrico},journal={Journal of Geophysical Research: Space Physics},volume={125},number={5},pages={e2019JA027618},year={2020},}
2019
2018
Chapter 9 - Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models
In this chapter, we give the reader an in-depth view into building of probabilistic forecasting models for geomagnetic time series using the Gaussian process methodology outlined in the previous chapters. We highlight design decisions and practical issues that must be addressed in order to use Gaussian process models for probabilistic prediction of a quantity of interest. As a pedagogical example, we formulate, train, and test a family of Gaussian process auto-regressive models for 1-h ahead prediction of the Dst geomagnetic index.
@incollection{CHANDORKAR2018237,title={Chapter 9 - Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models},editor={Camporeale, Enrico and Wing, Simon and Johnson, Jay R.},booktitle={Machine Learning Techniques for Space Weather},publisher={Elsevier},pages={237 - 258},year={2018},isbn={978-0-12-811788-0},doi={https://doi.org/10.1016/B978-0-12-811788-0.00009-3},url={https://www.sciencedirect.com/science/article/pii/B9780128117880000093},author={Chandorkar, Mandar and Camporeale, Enrico},keywords={Gaussian process, Space weather, Probabilistic forecasting, Geomagnetic indices},}
AGU
Multiple-hour-ahead forecast of the Dst index using a combination of long short-term memory neural network and Gaussian process
Gruet, Marina A, Chandorkar, M, Sicard, Angélica, and Camporeale, Enrico
@article{gruet2018multiple,title={Multiple-hour-ahead forecast of the Dst index using a combination of long short-term memory neural network and Gaussian process},author={Gruet, Marina A and Chandorkar, M and Sicard, Ang{\'e}lica and Camporeale, Enrico},journal={Space Weather},volume={16},number={11},pages={1882--1896},year={2018},publisher={AGU},}
2017
AGU
Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach
@article{chandorkar2017probabilistic,title={Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach},author={Chandorkar, M and Camporeale, E and Wing, S},journal={Space Weather},volume={15},number={8},pages={1004--1019},year={2017},publisher={American Geophysical Union (AGU)},}
2016
AGU
On the propagation of uncertainties in radiation belt simulations
Camporeale, Enrico, Shprits, Yuri, Chandorkar, Mandar, Drozdov, Alexander, and Wing, Simon
@article{camporeale2016propagation,title={On the propagation of uncertainties in radiation belt simulations},author={Camporeale, Enrico and Shprits, Yuri and Chandorkar, Mandar and Drozdov, Alexander and Wing, Simon},journal={Space Weather},volume={14},number={11},pages={982--992},year={2016},publisher={AGU},}
2015
IEEE
Fixed-size least squares support vector machines: Scala implementation for large scale classification
@inproceedings{chandorkar2015fixed,author={Chandorkar, Mandar and Mall, Raghvendra and Lauwers, Oliver and Suykens, Johan AK and De Moor, Bart},booktitle={2015 IEEE Symposium Series on Computational Intelligence},pages={522--528},year={2015},organization={IEEE},}