Smart Analytics for Big Time-series Data

author: Yasushi Sakurai, Graduate School of Science and Technology, Kumamoto University
author: Christos Faloutsos, Computer Science Department, Carnegie Mellon University
published: Nov. 21, 2017,   recorded: August 2017,   views: 1253
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Description

Time-series event analysis is an important topic that has attracted huge interest in countless domains. Given a large collection of time series, such as IoT device data, web-click logs, electric medical records, how can we efficiently and effectively find typical patterns? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? What are the major tools for forecasting and outlier detection? Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability. The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that can help us find patterns in large-scale time-series sequences. We review the state of the art in four related fields: (1) similarity search, pattern discovery and summarization, (2) large-scale non-linear modeling, (3) big tensor analysis, and (4) real-time modeling and forecasting. The emphasis of the tutorial is to provide the intuition behind these powerful tools, which is usually lost in the technical literature, as well as to introduce case studies that illustrate their practical use.

Link to tutorial: http://www.cs.kumamoto-u.ac.jp/~yasuko/TALKS/17-KDD-tut/

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