n_training_examples

It computes the number of training examples that would have a KNN model
with the specified parameters.

Allows forecasting time series using nearest neighbors regression
Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon
is higher than 1, two multi-step ahead forecasting strategies can be used.
The model built is autoregressive, that is, it is only based on the
observations of the time series. The nearest neighbors used in a prediction
can be consulted and plotted.

Francisco Martinez

tsfknn

Time Series Forecasting Using Nearest Neighbors

n_training_examples function

<dl><dt>timeS</dt>
<dd>A numeric vector or time series of class <code>ts</code>.</dd>
<dt>h</dt>
<dd>A positive integer. Number of values to forecast.</dd>
<dt>lags</dt>
<dd>An integer vector in increasing order expressing the lags used
as autoregressive variables.</dd>
<dt>msas</dt>
<dd>A string indicating the Multiple-Step Ahead Strategy used when
more than one value is predicted. It can be "recursive" or "MIMO" (the
default).</dd></dl>

Arguments

Number of training examples — n_training_examples

<dl>

<dt>timeS</dt>
<dd>A numeric vector or time series of class <code>ts</code>.</dd>


<dt>h</dt>
<dd>A positive integer. Number of values to forecast.</dd>


<dt>lags</dt>
<dd>An integer vector in increasing order expressing the lags used
as autoregressive variables.</dd>


<dt>msas</dt>
<dd>A string indicating the Multiple-Step Ahead Strategy used when
more than one value is predicted. It can be "recursive" or "MIMO" (the
default).</dd>

</dl>

n_training_examples: Number of training examples

Description

Usage

Value

Arguments

Examples