Time Series Data Mining: A Unifying View

Abstract

Time series data are ubiquitous; large volumes of such data are routinely created in scientific, industrial, entertainment, medical and biological domains. Examples include ECG data, gait analysis, stock market quotes, machine health telemetry, search engine throughput volumes etc. VLDB has traditionally been home to much of the community's best research on time series, with three to eight papers on time series appearing in the conference each year. What do we want to do with such time series? Everything! Classification, clustering, joins, anomaly detection, motif discovery, similarity search, visualization, summarization, compression, segmentation, rule discovery etc. Rather than a deep dive in just one of these subtopics, in this tutorial I will show a surprisingly small set of high-level representations, definitions, distance measures and primitives can be combined to solve the first 90 to 99.9% of the problems listed above. The tutorial will be illustrated with numerous real-world examples created just for this tutorial, including examples from robotics, wearables, medical telemetry, astronomy, and (especially) animal behavior. Moreover, all sample datasets and code snippets will be released so that the tutorial attendees (and later, readers) can first reproduce the results demonstrated, before attempting similar analysis on their data.