Metadata-Version: 1.1
Name: seasonal
Version: 0.3.0
Summary: Estimate trend and seasonal effects in a timeseries
Home-page: https://github.com/welch/seasonal
Author: Will Welch
Author-email: github@quietplease.com
License: MIT
Description: seasonal
        ========
        Robustly estimate and remove trend and periodicity in a timeseries.
        
        `Seasonal` can recover sharp trend and period estimates from noisy
        timeseries data with only a few periods.  It is intended for
        estimating season, trend, and level when initializing structural
        timeseries models like Holt-Winters. Input samples are
        assumed evenly-spaced from a continuous-time signal with noise but
        no anomalies.
        
        In this package, trend removal is in service of isolating and
        estimating periodic (non-trend) variation. "trend" is in the sense of
        Cleveland's STL decomposition -- a lowpass smoothing of
        the data, rather than a single linear trend (though you can opt for
        this). Detrending is accomplishd by a coarse fitted spline or a median
        filter.
        
        The seasonal estimate will be a list of period-over-period averages at
        each seasonal offset. You may specify a period length, or have it
        estimated from the data. The latter is an interesting capability of
        this package.
        
        See README.md for details on installation, API, theory, and examples.
        
        Dependencies
        -------------
        package: numpy, scipy
        extras:  pandas, matplotlib
        
Keywords: timeseries,seasonality,seasonal adjustment,detrend,robust estimation,theil-sen,Holt-Winters
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
