Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability. Given a large collection of time series, such as web-click logs, electric medical records and motion capture sensors, 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? The objective of this talk 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. The emphasis of the talk 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.
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