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.
Added time-series section and Zumbach&Muller's paper on inhomogeneous operators 2014-09-20 19:39:21 +00:00			`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:`

Update to all READMEs for hosted content reorganization of so-called historical papers 2015-10-07 19:11:04 +00:00			`* [: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`
Added time-series section and Zumbach&Muller's paper on inhomogeneous operators 2014-09-20 19:39:21 +00:00
			`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.`