papers-we-love_papers-we-love/time_series/README.md

Important papers relating to time-series data

Time-series data presents specific but very common problems for efficient 
analysis, resulting in the need for columnar data stores and iterative 
one-pass processing.

The included documents are:

* [:scroll:](https://github.com/papers-we-love/papers-we-love/blob/master/time_series/operators-on-inhomogeneous-time-series.pdf) [Operators on Inhomogeneous Time Series](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=208278)  - Gilles O. Zumbach and Ulrich A. Müller

  We present a toolbox to compute and extract information from
  inhomogeneous (i.e. unequally spaced) time series. The toolbox
  contains a large set of operators, mapping from the space of
  inhomogeneous time series to itself.

  These operators are computationally efficient (time and memory-wise)
  and suitable for stochastic processes. This makes them attractive for
  processing high-frequency data in finance and other fields. Using a
  basic set of operators, we easily construct more powerful combined
  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).