Chrysoula Kosma

Chrysoula Kosma

ML Researcher, Centre Borelli, ENS Paris-Saclay

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publications

Time Series Forecasting Models Copy the Past: How to Mitigate

Published in 31st International Conference on Artificial Neural Networks, 2022

Recently neural network architectures have been widely applied to the problem of time series forecasting. Most of these models are trained by minimizing a loss function that measures predictions’ deviation from the real values. Read More

Recommended citation: Kosma, C., Nikolentzos, G., Xu, N. and Vazirgiannis, M. (2022). "Time Series Forecasting Models Copy the Past: How to Mitigate." Proceedings of the 31st International Conference on Artificial Neural Networks (pp. 366-378). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-15919-0_31

Neural Ordinary Differential Equations for Modeling Epidemic Spreading

Published in Transactions on Machine Learning Research, 2023

We focus on the susceptible-infectious-recovered (SIR) epidemic model on networks. Given that this model can be expressed by an intractable system of ordinary differential equations, we devise a simpler system that approximates the output of the model. Then, we capitalize on recent advances in neural ordinary differential equations and propose a neural architecture that can effectively predict the course of an epidemic on the network. Read More

Recommended citation: Kosma, C., Nikolentzos, G., Panagopoulos, G., Steyaert, J.M. and Vazirgiannis, M. (2023). "Neural Ordinary Differential Equations for Modeling Epidemic Spreading.." Transactions on Machine Learning Research. https://openreview.net/pdf?id=yrkJGne0vN

TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting

Published in International Conference on Complex Networks and Their Applications, 2023

Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series.

Recommended citation: Xu, N., Kosma, C. and Vazirgiannis, M. (2023). "TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting." Proceedings of the 12th International Conference on Complex Networks and Their Applications. (to appear).

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