fourier_series

Returns a matrix containing terms from a Fourier series, up to order K

A suite of auxiliary functions that enhance time series estimation and forecasting, including a robust anomaly detection routine based on Chen and Liu (1993) <doi:10.2307/2290724> (imported and wrapped from the 'tsoutliers' package), utilities for managing calendar and time conversions, performance metrics to assess both point forecasts and distributional predictions, advanced simulation by allowing the generation of time series components—such as trend, seasonal, ARMA, irregular, and anomalies—in a modular fashion based on the innovations form of the state space model and a number of transformation methods including Box-Cox, Logit, 'Softplus-Logit' and Sigmoid.

Alexios Ghalanos

tsaux

Time Series Forecasting Auxiliary Functions

Alexios Galanos

fourier_series function

<dl><dt>dates</dt>
<dd>a Date vector representing the length of the series for which
the fourier terms are required.</dd>
<dt>period</dt>
<dd>frequency of the underlying series, if NULL will try to
infer it from the difference in the Date vector.</dd>
<dt>K</dt>
<dd>maximum order of the Fourier terms.</dd></dl>

Arguments

Fourier terms for modeling seasonality — fourier_series

<dl>

<dt>dates</dt>
<dd>a Date vector representing the length of the series for which
the fourier terms are required.</dd>


<dt>period</dt>
<dd>frequency of the underlying series, if NULL will try to
infer it from the difference in the Date vector.</dd>


<dt>K</dt>
<dd>maximum order of the Fourier terms.</dd>

</dl>

fourier_series: Fourier terms for modeling seasonality

Description

Usage

Value

Arguments