How can we find patterns in a sequence of sensor measurements (eg., a sequence of temperatures, or water-pollutant measurements)? How can we compress it? What are the major tools for forecasting and outlier detection? 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 sensor sequences. Sensor data analysis becomes of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-sensor processing abilities. We review the state of the art in three related fields: (a) fast similarity search for time sequences, (b) linear forecasting with the traditional AR (autoregressive) and ARIMA methodologies, (c) Kalman filters, and (d) non-linear forecasting, for chaotic/self-similar time sequences, using lag-plots and fractals. The emphasis of the tutorial is to give the intuition behind these powerful tools, which is usually lost in the technical literature, as well as to give case studies that illustrate their practical use.

The pdf of the foils is here part A and part B

- Part 1: Similarity Search
- why we need similarity search
- distance functions (Euclidean, LP norms, time-warping)
- fast searching (R-trees)

- Part 2: feature extraction
- DFT, Wavelets
- SVD, ICA, FastMap

- Part 3: Linear Forecasting
- main idea behind linear forecasting
- AR methodology
- multivariate regression
- Recursive Least Squares
- de-trending; periodicities

- Part 4: Kalman filters
- intution and example: tracking moving objects
- linear dynamical systems, inference and learning
- original Kalman
- Kalman filters with parameter estimation

- Extensions
- Handling missing values
- Switching Kalman filters; particle filters

- Kalman filters at work
- Segmentation
- Compression,
- Parallel learning on SMP
- Motion stitching

- Part 5: Bursty traffic and fractals
- fractal dimension
- 80/20 law

- Part 6: Non-linear forecasting
- main idea: lag-plots
- definition and intuition
- algorithms for fast computation
- case studies

- Conclusions

Researchers that want to get up to speed with the major tools in time sequence analysis. Also, practitioners who want a concise, intuitive overview of the state of the art.

None. The emphasis is on the intuition behind all these mathematical tools.

Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, the Innovations award in KDD’10, 16 “best paper” awards, and several teaching awards. He has served as a member of the executive committee of SIGKDD; he has published over 200 refereed articles, 11 book chapters and one monograph. He holds five patents and he has given over 30 tutorials and over 10 invited distinguished lectures. His research interests include data mining for graphs and streams, fractals, database performance, and indexing for multimedia and bio-informatics data.

Lei Li is currently a Ph.D. candidate at Computer Science Department, Carnegie Mellon University. Lei Li is received his B.E. degree from the Department of Computer Science and Engineering, Shanghai Jiao Tong University. His research interests include machine learning, data mining, time series analysis, and motion capture data.

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