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).
