scholarly journals Bayesian Non-Parametric Mixtures of GARCH(1,1) Models

2012 ◽  
Vol 2012 ◽  
pp. 1-16
Author(s):  
John W. Lau ◽  
Ed Cripps
Keyword(s):  
Time Series ◽  
Dirichlet Process ◽  
Time Series Data ◽  
Monte Carlo Algorithm ◽  
Series Data ◽  
Garch Models ◽  
Nonparametric Models ◽  
Regime Changes ◽  
Standard And Poor's ◽  
Financial Index

Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process. The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. The methodology is illustrated on the Standard and Poor's 500 financial index.

scholarly journals Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model

2020 ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Aanzil Akram Halsana ◽  
Lutfunnesa Khatun ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Ductal Adenocarcinoma ◽  
Series Data ◽  
Main Text

ABSTRACTPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer (PC), late detection of which leads to its therapeutic failure. This study aims to find out key regulatory genes and their impact on the progression of the disease helping the etiology of the disease which is still largely unknown. We leverage the landmark advantages of time-series gene expression data of this disease, and thereby the identified key regulators capture the characteristics of gene activity patterns in the progression of the cancer. We have identified the key modules and predicted gene functions of top genes from the compiled gene association network (GAN). Here, we have used the natural cubic spline regression model (splineTimeR) to identify differentially expressed genes (DEG) from the PDAC microarray time-series data downloaded from gene expression omnibus (GEO). First, we have identified key transcriptomic regulators (TR) and DNA binding transcription factors (DbTF). Subsequently, the Dirichlet process and Gaussian process (DPGP) mixture model is utilized to identify the key gene modules. A variation of the partial correlation method is utilized to analyze GAN, which is followed by a process of gene function prediction from the network. Finally, a panel of key genes related to PDAC is highlighted from each of the analyses performed.Please note: Abbreviations should be introduced at the first mention in the main text – no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

scholarly journals Clustering gene expression time series data using an infinite Gaussian process mixture model

2017 ◽  
Author(s):  
Ian C. McDowell ◽  
Dinesh Manandhar ◽  
Christopher M. Vockley ◽  
Amy K. Schmid ◽  
Timothy E. Reddy ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Cellular Response ◽  
Time Series Data ◽  
Series Data ◽  
Nonparametric Model ◽  
Cluster Number ◽  
Temporal Dependencies

AbstractTranscriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models cluster number with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison with state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal novel regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

scholarly journals GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics

2001 ◽  
Vol 15 (4) ◽  
pp. 157-168 ◽  
Author(s):  
Robert Engle
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Garch Model ◽  
Applied Econometrics ◽  
Series Data ◽  
Garch Models ◽  
Portfolio Risk ◽  
Financial Applications ◽  
Analysis Of Time Series

ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. This paper gives the motivation behind the simplest GARCH model and illustrates its usefulness in examining portfolio risk. Extensions are briefly discussed.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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