piecewise_logistic

Evaluate the piecewise logistic function.

internal

Implements a procedure for forecasting time series data based on
an additive model where non-linear trends are fit with yearly, weekly, and
daily seasonality, plus holiday effects. It works best with time series
that have strong seasonal effects and several seasons of historical data.
Prophet is robust to missing data and shifts in the trend, and typically
handles outliers well.

Cuong Duong

prophet

Automatic Forecasting Procedure

Sean Taylor

Ben Letham

piecewise_logistic function

<dl><dt>t</dt>
<dd>Vector of times on which the function is evaluated.</dd>
<dt>cap</dt>
<dd>Vector of capacities at each t.</dd>
<dt>deltas</dt>
<dd>Vector of rate changes at each changepoint.</dd>
<dt>k</dt>
<dd>Float initial rate.</dd>
<dt>m</dt>
<dd>Float initial offset.</dd>
<dt>changepoint.ts</dt>
<dd>Vector of changepoint times.</dd></dl>

Arguments

Evaluate the piecewise logistic function. — piecewise_logistic

<dl>

<dt>t</dt>
<dd>Vector of times on which the function is evaluated.</dd>


<dt>cap</dt>
<dd>Vector of capacities at each t.</dd>


<dt>deltas</dt>
<dd>Vector of rate changes at each changepoint.</dd>


<dt>k</dt>
<dd>Float initial rate.</dd>


<dt>m</dt>
<dd>Float initial offset.</dd>


<dt>changepoint.ts</dt>
<dd>Vector of changepoint times.</dd>

</dl>

piecewise_logistic: Evaluate the piecewise logistic function.

Description

Usage

Value

Arguments