Katharina's paper *Dependent state space Student-t processes for imputation and data augmentation in plasma diagnostics* (Rath et al.) got accepted at *Contributions to Plasma Physics*!
Published on April 13, 2023 by Data Science @ LMU Munich
katharina physics processes
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Multivariate time series measurements in plasma diagnostics present several challenges when training machine learning models: the availability of only a few labeled data increases the risk of overfitting, and missing data points or outliers due to sensor failures pose additional difficulties. To overcome these issues, we introduce a fast and robust regression model that enables imputation of missing points and data augmentation by massive sampling while exploiting the inherent correlation between input signals. The underlying Student-t process allows for a noise distribution with heavy tails and thus produces robust results in the case of outliers. We consider the state space form of the Student-t process, which reduces the computational complexity and makes the model suitable for high-resolution time series. We evaluate the performance of the proposed method using two test cases, one of which was inspired by measurements of flux loop signals.