Harmonic Fit to Time Series — harmonicfit • iAR

This function fit an k-harmonic function to time series data.

Usage

harmonicfit(x, ...)

Arguments

x

An object of class `utilities`, or `NULL` (the default). When `x` is `NULL` or omitted, the `utilities` object is created internally, so `harmonicfit(data = clcep, f1 = f1)` is the recommended usage.

...

Additional arguments for pairing time series:

data: A data frame with three columns corresponding to the time, values, and standard errors of the irregularly observed time series.
f1: frequency (1 / period) of the time series.
nham: Number of harmonic components in the model.
weighted: logical; if true, a weighted least squares (WLS) estimation is performed using weights based on the standard deviations of the errors. Default is 'FALSE'.
remove_trend: logical; if true, the linear trend of time series will be removed before the the harmonic model is fitted.

Value

An object of class `utilities` with the slots:

fitted_values: Fitted values from the harmonic model.
residuals: Residuals from the harmonic model.
coef: Estimated coefficients of the harmonic model.
summary: A summary object containing detailed model information.

Details

The function fits a harmonic regression model with 'nham' components to the input time series, optionally removing a linear trend and allowing for weighted estimation when standard errors are available.

References

Elorrieta F, Eyheramendy S, Palma W, Ojeda C (2021). “A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series.” Monthly Notices of the Royal Astronomical Society, 505(1), 1105-1116. ISSN 0035-8711. doi:10.1093/mnras/stab1216 . https://academic.oup.com/mnras/article-pdf/505/1/1105/38391762/stab1216.pdf.

Examples

data(clcep)
f1=0.060033386
o1=harmonicfit(data=clcep,f1=f1)
head(o1@fitted_values)
#>           1           2           3           4           5           6 
#> -0.01307640  0.16116566 -0.08189031 -0.08047315 -0.07301615  0.26554040 
head(o1@residuals)
#>            1            2            3            4            5            6 
#> -0.020520255 -0.022710532 -0.016366868  0.028516019  0.003159220 -0.002247028