Estimate trend and seasonal effects in a timeseries
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-time signal with noise but no anomalies.
The trend estimate is a single slope. No trend lasts forever, but the assumption is that the provided data is of a duration to be characterized by a single trend.
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
numpy, scipy
