Structural learning with time‐varying components: tracking the cross‐section of financial time series

By Makram Talih, N. Hengartner. 2005

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bernhard Schölkopf
2. Jan Peters
3. Kun Zhang
4. Peter Buhlmann
5. Eric P. Xing
6. Clark Glymour
7. Biwei Huang
8. Dominik Janzing

In Information Systems

1. J. Ramsey
2. Jan Peters
3. Bernhard Schölkopf
4. Peter Buhlmann
5. Clark Glymour
6. Kun Zhang
7. Eric P. Xing
8. Le Song

Citations in the corpus

• Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Improved versions include the online change point detection method (Adams and Mackay, 2007), the online undirected graph learning (Talih and Hengartner, 2005), and the locally stationary structure tracker algorithm (Kummerfeld and Danks, 2013).”
• Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “Improved versions include the online changepoint detection method [ Adams and Mackay, 2007 ] , the online undirected graph learning [Talih and Hengartner, 2005], the locally stationary structure tracker algorithm [Kummerfeld and Danks, 2013], and the regime aware learning algorithm to learn a…”
• Invariant Causal Prediction for Sequential Data (2017, background): “A further related area of research extends noninstantaneous effects models by allowing for time-dependent parameters (e.g., Talih and Hengartner 2005; Siracusa and Fisher III 2009).”
• Time-Varying Dynamic Bayesian Networks (2009, background): “Recently, there has been a surge of interests along this direction [5, 6, 7, 8, 9, 10].”
• Homogeneity and change-point detection tests for multivariate data using rank statistics (2011, background): “…al., 2006; Lévy-Leduc and Roueff, 2009), processing of audiovisual data (Désobry et al., 2005), financial and econometric modelling (Bai and Perron, 2003; Talih and Hengartner, 2005), health monitoring (Brodsky and Darkhovsky, 2000), or bioinformatics (Picard et al., 2005; Vert and Bleakley, 2010).”
• Statistical inference of the time-varying structure of gene-regulation networks (2010, methodology): “Talih and Hengarten [10] developed a Markov Chain Monte Carlo (MCMC) methodology to recover time-varying Gaussian graphical models in a financial context.”
• A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “This arises in financial modeling (Talih and Hengartner 2005), where correlated assets are traded and models are based on historical data represented as multivariate time series.”
• Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “Improved versions include the online changepoint detection method [12], the online undirected graph learning [13], the locally stationary structure tracker algorithm [14].”
• ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data (2013, background): “This arises in financial modeling (Talih and Hengartner, 2005), where correlated assets are traded and models are based on historical data.”
• Tracking Time-varying Graphical Structure (2013, background): “Two previous papers have aimed to learn time-indexed graph structures from time-series data, though both require full datasets as input, so cannot function in real-time [14, 11].”