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31 lines
1.7 KiB
Markdown
31 lines
1.7 KiB
Markdown
Important papers relating to time-series data
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Time-series data presents specific but very common problems for efficient
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analysis, resulting in the need for columnar data stores and iterative
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one-pass processing.
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The included documents are:
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* [: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
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We present a toolbox to compute and extract information from
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inhomogeneous (i.e. unequally spaced) time series. The toolbox
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contains a large set of operators, mapping from the space of
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inhomogeneous time series to itself.
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These operators are computationally efficient (time and memory-wise)
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and suitable for stochastic processes. This makes them attractive for
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processing high-frequency data in finance and other fields. Using a
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basic set of operators, we easily construct more powerful combined
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operators which cover a wide set of typical applications.
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* [:scroll:](https://github.com/papers-we-love/papers-we-love/blob/master/time_series/ts-asap.pdf)
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[ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization](http://futuredata.stanford.edu/asap/) - Kexin Rong, Peter Bailis
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Time Series smoothing method to better prioritize attention in time series
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exploration and monitoring visualizations, smooth the time series as much as
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possible to remove noise while still retaining large-scale structure. We
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develop a new technique for automatically smoothing streaming time series
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that adaptively optimizes this trade-off between noise reduction (i.e.,
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variance) and outlier retention (i.e., kurtosis).
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