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Time-series new paper: ASAP (#446)

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* Add entry for asap.
* Add ASAP pdf
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David Rio Deiros 2017-05-01 12:42:35 -04:00 committed by Zeeshan Lakhani
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@ -17,7 +17,14 @@ The included documents are:
and suitable for stochastic processes. This makes them attractive for and suitable for stochastic processes. This makes them attractive for
processing high-frequency data in finance and other fields. Using a processing high-frequency data in finance and other fields. Using a
basic set of operators, we easily construct more powerful combined basic set of operators, we easily construct more powerful combined
operators which cover a wide set of typical applications. operators which cover a wide set of typical applications.
* [:scroll:](https://github.com/papers-we-love/papers-we-love/blob/master/time_series/ts-asap.pdf)
[ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization](http://futuredata.stanford.edu/asap/) - Kexin Rong, Peter Bailis
Time Series smoothing method to better prioritize attention in time series
exploration and monitoring visualizations, smooth the time series as much as
possible to remove noise while still retaining large-scale structure. We
develop a new technique for automatically smoothing streaming time series
that adaptively optimizes this trade-off between noise reduction (i.e.,
variance) and outlier retention (i.e., kurtosis).

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