fport_eda: Exploratory Data Analysis for Functional Time Series.

In FTSgof: White Noise and Goodness-of-Fit Tests for Functional Time Series

Exploratory Data Analysis for Functional Time Series.

Description

This function sequentially displays the fACF plot, the fSACF and the rainbow plot of a functional time series (FTS) for comprehensive exploratory data analysis.

Usage

fport_eda(f_data, H = 20, alpha = 0.05, wwn_bound = FALSE, M = NULL)

Arguments

f_data	A J \times N matrix of FTS data, where J is the number of discrete points in a grid and N is the sample size.
H	A positive integer representing the maximum lag for computing the coefficients and confidence bounds. This value determines the range of lags included in the fACF and fSACF plots.
alpha	A numeric value between 0 and 1 indicating the significance level for the confidence bounds in the fACF and fSACF plots.
wwn_bound	A Boolean value allowing the user to turn on the WWN bound in the fACF plot. FALSE by default. Speeds down computation when TRUE.
M	A positive integer value. The number of Monte-Carlo simulations used to compute the confidence bounds under the WWN assumption. If M = NULL, M = \text{floor}((\max(150 - N, 0) + \max(100 - J, 0) + (J / \sqrt{2}))), ensuring that the number of Monte Carlo simulations is adequate based on the dataset size.

Details

This function sequentially displays the rainbow plot, the fACF plot, and the fSACF of an FTS for comprehensive exploratory data analysis. See the help page of rainbow3D, fACF, fSACF, for more details.

Value

A 3D rainbow plot, a fACF plot for lags h \in 1:H with the WWN (1-\alpha)100 \% upper confidence bound and the constant strong white noise (SWN) (1-\alpha)100 \% upper confidence bound, and a fSACF plot for lags h \in 1:H with the SWN (1-\alpha)100 \% upper and lower confidence bounds.

References

[1] Kokoszka P., Rice G., Shang H.L. (2017). Inference for the autocovariance of a functional time series under conditional heteroscedasticity. Journal of Multivariate Analysis, 162, 32-50.

[2] Mestre G., Portela J., Rice G., Roque A. M. S., Alonso E. (2021). Functional time series model identification and diagnosis by means of auto-and partial autocorrelation analysis. Computational Statistics & Data Analysis, 155, 107108.

[3] Yeh CK, Rice G, Dubin JA (2023). “Functional spherical autocorrelation: A robust estimate of the autocorrelation of a functional time series.” Electronic Journal of Statistics, 17, 650–687.

Examples

data(Spanish_elec) # Daily Spanish electricity price profiles
fport_eda(Spanish_elec)

FTSgof documentation built on Oct. 4, 2024, 1:06 a.m.