setup_dataframe

Adds a time index and scales y. Creates auxiliary columns 't', 't_ix',
'y_scaled', and 'cap_scaled'. These columns are used during both fitting
and predicting.

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

setup_dataframe function

<dl><dt>m</dt>
<dd>Prophet object.</dd>
<dt>df</dt>
<dd>Data frame with columns ds, y, and cap if logistic growth. Any
specified additional regressors must also be present.</dd>
<dt>initialize_scales</dt>
<dd>Boolean set scaling factors in m from df.</dd></dl>

Arguments

Prepare dataframe for fitting or predicting. — setup_dataframe

<dl>

<dt>m</dt>
<dd>Prophet object.</dd>


<dt>df</dt>
<dd>Data frame with columns ds, y, and cap if logistic growth. Any
specified additional regressors must also be present.</dd>


<dt>initialize_scales</dt>
<dd>Boolean set scaling factors in m from df.</dd>

</dl>

setup_dataframe: Prepare dataframe for fitting or predicting.

Description

Usage

Value

Arguments