Estimate trend and seasonal effects in a timeseries
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.
- package: numpy, scipy
- extras: pandas, matplotlib
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size seasonal-0.3.1-py2.py3-none-any.whl (18.1 kB)||File type Wheel||Python version 2.7||Upload date||Hashes View hashes|
Hashes for seasonal-0.3.1-py2.py3-none-any.whl