# sdrt-vignette

## Overview

The sdrt R package provides powerful capabilities for estimating a basis for the Time Series Central Mean Subspace (TS-CMS). This vignette serves as a comprehensive guide to the functionalities offered by the sdrt package, utilizing the lynx dataset as an illustrative example.

## Chapter 1: Installation of itdr package

The itdr R package can be installed in two ways:

• From the Comprehensive R Archive Network (CRAN):

The following code chunk demonstrate the installation of the package using the install.packages() function in R. Then, the library can be import into the current R session using the library() function.

install.packages("sdrt")
library(sdrt)
• From GitHub:

The development version of sdrt R package can be installed form GitHub.

library(devtools)
install_github("TharinduPDeAlwis/sdrt")
library(sdrt)

## Chapter 2: Fourier Transformation Method to Estimate the TS-CMS.

In this section, we demonstrate the functions within the sdrt package that utilize the Fourier method to estimate the sufficient dimension reduction (SDR) subspaces in time series.

In Section 2.1 brings the estimation of model parameters. The tuning the hyper parameter in Section 2.2 and the estimation of a basis of the TS-CMS explains in Section 2.3.

### 2.1: Estimating Model Parameters

The first step is to estimate the model parameters, specifically p and d. The block bootstrap procedure is employed to estimate the unknown lags number (p) and the unknown dimension of the TS-CMS (d). For more details refer to Samadi and De Alwis (2023). The following R code chunk demonstrate the use of the pd.boots() function to estimate p and d.

data("lynx")
y <- log10(lynx)
p_list=seq(2,5,by=1)
fit.model=pd.boots(y,p_list,w1=0.1,B=10)
fit.model$dis_pd fit.model$p_hat
fit.model$d_hat In this example, the estimated lag number and the dimension of the TS-CMS are denoted as p_hat=3 and d_hat=1 for the lynx dataset. ### 2.2: Tuning the Model Parameter Within the Fourier method for estimating the TS-CMS, there exists a hyperparameter $$\sigma_u^2$$. While a recommended value of $$\sigma_u^2=0.1$$ exists, the sdrt package offers users the flexibility to fine-tune this parameter for each dataset. The following R code chunk outlines the procedure using the sigma_u() function to tune this parameter for the lynx dataset. set.seed(1) data("lynx") y <- log10(lynx) p <- 3 d <- 1 w1_list=seq(0.1,0.5,by=0.1) Tunning.model=sigma_u(y, p, d, w1_list=w1_list, std=FALSE, B=10) Tunning.model$sigma_u_hat

In this example, the estimated tuning parameter for the lynx dataset is $$\sigma_u^2=0.3$$. Users can adapt this process to optimize the hyperparameter for their specific dataset.

### 2.3: Estimating the TS-CMS

We have described the estimation procedure of model parameters, p and d in Section 2.1 and the tuning parameter $$\sigma_u^2$$ in Section 2.2. Now, we are ready to estimate the Time Series Central Mean Subspace (TS-CMS). The TS-CMS can be estimated using the sdrt() function by passing the method argument as "FM". The following R code chunk illustrate the use of the sdrt() function for this purpose.

library(sdrt)
data("lynx")
y <- log10(lynx)
p <- 3
d <- 1
fit.model <- sdrt(y, p, d=1,method="FM",density = "kernel")
fit.model$eta_hat In this example, the estimated TS-CMS is obtained using the Fourier method (“FM”). Users can customize the parameters according to their dataset and analysis needs. ## Chapter 3: Nadaraya-Watson Method to Estimate the TS-CMS. In this section, we demonstrate the application of the sdrt() function within the sdrt R package to estimate the TS-CMS using the Nadaraya-Watson (NW) method, as proposed by Park et al. (2009). The following R code chunk illustrates the utilization of the sdrt() on the lynx dataset. Here, we specify the method argument as "NW" to indicate the Nadaraya-Watson estimation method. library(pracma) data("lynx") y <- log10(lynx) p <- 4 d <- 1 fit.model <- sdrt(y, p, d,method="NW") fit.model$eta_hat

In this example, the Nadaraya-Watson method is employed to estimate the TS-CMS, and users can adjust parameters based on their specific dataset requirements.