HST_stock

A subset of daily stock price data of HST from November 7, 2011 to November
5, 2021.

datasets

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call 'dSTEM' is introduced to detect change points based on the 'STEM' algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Zhibing He

dSTEM

Multiple Testing of Local Extrema for Detection of Change Points

HST_stock function

A data frame with 2,517 rows and 6 columns:<dl>
 <dt>Date</dt>
<dd>date from November 7, 2011 to November 5, 2021</dd> <dt>Close, Open, High, Low</dt>
<dd>stock price</dd> <dt>Volume</dt>
<dd>stock exchange volume</dd>
</dl>

Format

Stock price of Host &amp; Hotel Resorts (HST) — HST_stock

A data frame with 2,517 rows and 6 columns:<dl>
 <dt>Date</dt>
<dd>date from November 7, 2011 to November 5, 2021</dd>

 <dt>Close, Open, High, Low</dt>
<dd>stock price</dd>

 <dt>Volume</dt>
<dd>stock exchange volume</dd>


</dl>

HST_stock: Stock price of Host & Hotel Resorts (HST)

Description

Usage

Arguments

Format