harvest

<p>A univariate time series object of class "ts". Frequency is set to 23 -- the approximate number of observations per year.</p>

datasets

BFAST integrates the decomposition of time series into trend,
seasonal, and remainder components with methods for detecting
and characterizing abrupt changes within the trend and seasonal
components. BFAST can be used to analyze different types of
satellite image time series and can be applied to other disciplines
dealing with seasonal or non-seasonal time series, such as hydrology,
climatology, and econometrics. The algorithm can be extended to
label detected changes with information on the parameters of the
fitted piecewise linear models. BFAST monitoring functionality is added
based on a paper that has been submitted to Remote Sensing of Environment.
BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models.
BFAST approach is flexible approach that handles missing data without interpolation.
Furthermore now different models can be used to fit the time series data and detect structural changes (breaks).

Jan Verbesselt

bfast

Breaks For Additive Season and Trend (BFAST)

Achim Zeileis

Rob Hyndman

harvest function

16-day NDVI time series for a Pinus radiata plantation. — harvest

16-day NDVI time series for a Pinus radiata plantation.

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harvest: 16-day NDVI time series for a Pinus radiata plantation.

Description

Usage

Arguments

Examples