# predictiveDFT

0th

Percentile

##### Predictive DFT.

Function to calculate the predictive DFT.

##### Usage
predictiveDFT(x, freq = 2 * pi * (1:length(x))/length(x), taper = FALSE, ar = NULL, ...)
##### Arguments
x

time series vector.

freq

frequency vector.

taper

logical vector. If TRUE, use the tapered time series to estimate an AR coefficients. Default is FALSE.

ar

predetermined AR coefficients to evaluate the predictive DFT. Default is NULL.

Arguments passing ar() function.

##### Details

If ar vector is predetermined, then we evaluate the predictive DFT using the this AR coefficient vector (see eq (2.2) of the reference). If ar is not specified (default), then we fit the best fitting AR(p) model using AIC and Yule-Walker estimator.

##### Value

The complex valued predictive DFT vector.

##### References

S. Das, S. Subba Rao, and J. Yang. Spectral methods for small sample time series: A complete periodogram approach. Submitted, 2020.

complete.pgram, tapered.complete.pgram

##### Aliases
• predictiveDFT
##### Examples
# NOT RUN {
set.seed(123)
x <- arima.sim(model=list(ar=0.7), n=100)

v1 <- predictiveDFT(x) #default
v2 <- predictiveDFT(x,ar=c(0.7)) #predictive DFT using AR(1) model with coefficient: 0.7.
v3 <- predictiveDFT(x,taper=TRUE) #Using tapered time series to fit the best AR model.
v4 <- predictiveDFT(x, method="ols") #Using ols method to fit the best AR model.

# }

Documentation reproduced from package cspec, version 0.1.2, License: GPL-2

### Community examples

Looks like there are no examples yet.