This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
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Project Description

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 real-valued signal with noise but no anomalies.

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.

Trend removal in this package is in service of isolating and estimating the periodic (non-trend) variation. A lowpass smoothing of the data is removed from the original series, preserving original seasonal variation. Detrending is accomplishd by a coarse fitted spline, mean or median filters, or a fitted line.

See https://github.com/welch/seasonal/README.md for details on installation, API, theory, and examples.

Dependencies

  • package: numpy, scipy
  • extras: pandas, matplotlib
Release History

Release History

0.3.1

This version

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0.3.0

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0.2.0

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0.1.0

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0.0.0

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Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
seasonal-0.3.1-py2.py3-none-any.whl (18.1 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Jun 11, 2016

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