TSFuse: automated feature construction for multiple time series data

A central paradigm for building predictive models from time series data is to convert the data into a feature vector representation and then apply standard inductive learners. Typically, the conversion is done by manually defining features, which is an extremely time-consuming and error-prone process. This has motivated the development of algorithms that automatically construct features from time series. However, these systems are typically designed for univariate time series data. In contrast, many real-world applications require analyzing time series consisting of data collected by multiple sensors. In this context, it is often useful to derive new series by fusing the collected data both within a sensor and across multiple different sensors. Unfortunately, this poses additional challenges for automated construction as exponentially more operations are possible than in the univariate case. This paper proposes an automated feature construction system called TSFuse, which supports fusion and explores the search space in a computationally efficient way. We perform an empirical evaluation on real-world time series classification datasets and show that our system is able to find a better feature representation compared to existing feature construction systems for univariate time series data.