Dependent state space Student‐t processes for imputation and data augmentation in plasma diagnostics

Abstract

AbstractMultivariate 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.