Efficient Kernel-based Subsequence Search for User Identification from Walking Activity
This work addresses a known bottleneck in time-series analysis for applications like wearable sensor data, but it is incremental as it builds on existing kernel and embedding methods.
The paper tackles the computational inefficiency of Dynamic Time Warping (DTW) for subsequence search in data streams by learning a kernel to approximate DTW, reducing computational burden, and validates it on a benchmark dataset for user identification from walking activity.
This paper presents an efficient approach for subsequence search in data streams. The problem consists in identifying coherent repetitions of a given reference time-series, eventually multi-variate, within a longer data stream. Dynamic Time Warping (DTW) is the metric most widely used to implement pattern query, but its computational complexity is a well-known issue. In this paper we present an approach aimed at learning a kernel able to approximate DTW to be used for efficiently analyse streaming data collected from wearable sensors, reducing the burden of computation. Contrary to kernel, DTW allows for comparing time series with different length. Thus, to use a kernel, a feature embedding is used to represent a time-series as a fixed length vector. Each vector component is the DTW between the given time-series and a set of 'basis' series, usually randomly chosen. The vector size is the number of basis series used for the feature embedding. Searching for the portion of the data stream minimizing the DTW with the reference subsequence leads to a global optimization problem. The proposed approach has been validated on a benchmark dataset related to the identification of users depending on their walking activity. A comparison with a traditional DTW implementation is also provided.