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Publicatie

Methods for Automating Time Series Analysis with Applications in Healthcare

Boek - Dissertatie

Time series, i.e., data collected from processes that change over time, are collected more often than we think. For example, activity trackers continuously record our heart rate, stock traders observe daily stock prices, and weather forecasters carefully analyse meteorological data. Time series typically contain a large number of observations, which makes analysing these observations by hand a complex and time-consuming task. Therefore, we need tools that support practitioners by automating time series analysis. In this dissertation, we focus on two time series analysis tasks: feature construction and change point detection. We aim to address three challenges that are not solved by existing automated time series analysis methods. First, current feature construction methods only analyse each time series individually. By not exploiting relations between multiple series, these methods may miss important features. Second, automated feature construction methods are completely data-driven. However, domain experts can often make suggestions about which kind of features are potentially relevant. Unfortunately, existing methods are not able to incorporate these suggestions. Third, change point detection is currently tackled in either a fully supervised or fully unsupervised setting. On the one hand, supervised methods can find an accurate segmentation by exploiting labels. However, these methods require annotating the data, which is a time-consuming process. On the other hand, unsupervised methods require no labels, but have to make assumptions about how the underlying statistics of the time series correlate to the series' state. Unfortunately, making incorrect assumptions may lead to different change points than expected. This dissertation makes five main contributions. The first two contributions present two automated time series analysis approaches that address the challenges described above. First, we propose an automated feature construction method that exploits relations between multiple time series by fusing multiple series. Our approach can incorporate domain knowledge in the form of metadata and compatibility constraints. Second, we propose a semi-supervised change point detection method that uses active learning to obtain labels. In the remaining three contributions, we evaluate the performance of our automated feature construction approach on real-world time series data in health applications. First, we develop an activity recognition model and propose techniques to improve the model's performance on real-world data collected from patients. Second, we develop a joint loading estimation model based on data collected by a mobile phone. Third, we compare the performance of hand-crafted and automatically constructed features for epileptic seizure detection.
Jaar van publicatie:2022
Toegankelijkheid:Open