SIGMOD 2015 TUTORIAL
Mining and Forecasting of Big Time-series DataYasushi Sakurai, Yasuko Matsubara (Kumamoto U) and Christos Faloutsos (CMU/SCS)
Yasushi Sakurai | Yasuko Matsubara | Christos Faloutsos |
Description
Description (pdf):
[PDF]
Abstract:
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? 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 and pattern discovery, (2)
linear modeling and summarization, (3) non-linear modeling and
forecasting, and (4) the extension of time-series mining and tensor
analysis. 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.
Foils
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Part0: Introduction
[note] [full]
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Part1: Similarity search, pattern discovery and summarization
- Part1a: [note] [full]
- Part1b: [note] [full] -
Part2: Non-linear modeling and forecasting
[note] [full] -
Part3: Extension of time-series data: tensor analysis
[note] [full] -
Part4: Conclusions
[note] [full]
Software
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Please visit
[here]