Cover Image

Learn about the generalized autoregressive conditional heteroskedasticity (GARCH) model in R with data from the DJIA 30 stock time series (2018) /

This dataset is designed for teaching the generalized autoregressive conditional heteroskedasticity (GARCH) model for a univariate time series. The dataset is a subset of data derived from the 2018 DJIA 30 Stock Time Series dataset, and the example examines the time series of daily closing price of...

Full description

Bibliographic Details
Main Author:	Shi, Feng, active 2019 (Author)
Format:	eBook
Language:	English
Published:	London : SAGE Publications Ltd., 2019.
Subjects:	GARCH model > Study and teaching. R (Computer program language) Time-series analysis. Modèle GARCH > Étude et enseignement. R (Langage de programmation) Série chronologique. Time-series analysis
Online Access:	Connect to the full text of this electronic book

Similar Items

Learn about the Hawkes process in R with data from the DJIA 30 stock time series (2018) /
by: Shi, Feng, active 2019
Published: (2019)

Learn about the poisson process in R with data from the DJIA 30 stock time series (2018) /
by: Shi, Feng, active 2019
Published: (2019)

Hands-On Time Series Analysis with R : Perform Time Series Analysis and Forecasting Using R.
by: Krispin, Rami
Published: (2019)

Time series analysis.
Published: (2018)

Nonlinear time series analysis with R /
by: Huffaker, Ray C., et al.
Published: (2018)

Multivariate time series analysis : with R and financial applications /
by: Tsay, Ruey S., 1951-
Published: (2014)

Time series forecasting using foundation models /
by: Peixeiro, Marco
Published: (2026)

Analysis of integrated and cointegrated time series with R : with 19 figures /
by: Pfaff, Bernhard
Published: (2006)

Hidden Markov models for time series : an introduction using R /
by: Zucchini, W.
Published: (2009)

Basic data analysis for time series with R /
by: Derryberry, DeWayne R.
Published: (2014)

Shi jian xu lie fen xi shi zhan : ji yu ji qi xue xi he tong ji xue = Practical time series analysis /
by: Nielsen, Aileen
Published: (2022)

Time series forecasting /
by: Sharer, Elizabeth, active2023
Published: (2023)

Time series analysis : methods and applications /
Published: (2012)

Applied time series analysis II : proceedings of the Second Applied Time Series Symposium held in Tulsa, Oklahoma, March 3-5, 1980 /
Published: (1981)

Time series for data scientists : data management, description, modeling and forecasting /
by: Sánchez, Juana
Published: (2023)

The analysis of time series : an introduction /
by: Chatfield, Christopher
Published: (1989)

Introduction to multiple time series analysis /
by: Lütkepohl, Helmut
Published: (1991)

Time series in the time domain /
Published: (1985)

Time series in the frequency domain /
Published: (1983)

Recent developments in time series /
Published: (2003)

Time series analysis for social sciences /
by: Box-Steffensmeier, Janet M., 1965-, et al.
Published: (2014)

Displaying time series, spatial, and space-time data with R /
by: Perpiñán Lamigueiro, Óscar
Published: (2014)

Time series package (TSPack) /
Published: (1985)

Time series forecasting in Python /
by: Peixeiro, Marco
Published: (2022)

A practical approach to timeseries forecasting using Python.
Published: (2023)

Data science.
Published: (2023)

The analysis of time series : theory and practice /
by: Chatfield, Christopher
Published: (1975)

Dynamic modelling of time-to-event processes /
by: Ladde, Gangaram S., et al.
Published: (2025)

Univariate time-series analysis of quarterly earnings : some unresolved issues /
by: Hopwood, William S.
Published: (1986)

Smoothness priors analysis of time series /
by: Kitagawa, G. (Genshiro), 1948-
Published: (1996)

Doing time series analyses of income inequalitypooled or comparative? /
by: Flaherty, Eoin
Published: (2018)

Time series decomposition and seasonal adjustment /
by: Zong, Ping
Published: (2026)

Multichannel time series analysis with digital computer pograms /
by: Robinson, Enders A.
Published: (1983)

Multichannel time series analysis with digital computer programs /
by: Robinson, Enders A.
Published: (1967)

Multichannel time series analysis with digital computer programs /
by: Robinson, Enders A.
Published: (1978)

Machine Learning for Time-Series with Python Forecast, Predict, and Detect Anomalies with State-Of-the-art Machine Learning Methods.
by: Auffarth, Ben
Published: (2021)

Time Series Algorithms Recipes : Implement Machine Learning and Deep Learning Techniques with Python /
by: Kulkarni, Akshay R., et al.
Published: (2023)

Time series methods in hydrosciences : proceedings of an international conference held at Canada Centre for Inland Waters, Burlington, Ontario, Canada, October 6-8, 1981 /
Published: (1982)

Dynamic time series models using R-INLA : an applied perspective /
by: Ravishanker, Nalini, et al.
Published: (2023)

Periodic time series models /
by: Franses, Philip Hans, 1963-
Published: (2004)