7 May 2021

Speaker: Lynna Chu (Iowa State University)

Title: Sequential Change-point Detection for High-Dimensional and non-Euclidean Data

Abstract - In many modern applications, high-dimensional/non-Euclidean data sequences are collected to study complex phenomena over time and it is often of scientific significance to detect anomaly events as data is continually being collected. We study a nonparametric framework that utilizes nearest neighbor information among the observations and can be applied to various data types to detect changes in an online setting. We consider new test statistics under this framework that can detect anomaly events more effectively than the existing test with the false discovery rate controlled at the same level. Analytical formulas to determine the threshold of claiming a change are also provided, making the approach easily applicable for real data applications.


21 May 2021 - 3-4pm UK time

Speaker: Hao Ni (University College London)

Title: Sig-Wasserstein Generative models to generate realistic synthetic time series. 

Abstract - Wasserstein generative adversarial networks (WGANs) have been very successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Moreover, training WGANs is computational expensive due to the min-max formulation of the loss function. To overcome these challenges, we integrate Wasserstein GANs with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal and principled description for a stream of data, and its expected value characterises the law of the time-series model. In particular, we develop a new metric, (conditional) Sig-W1, that captures the (conditional) joint law of time series models, and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and our method achieved the superior performance than the other state-of-the-art benchmark methods. This is the joint work with Lukasz Szpruch (Uni of Edinburgh), Magnus Wiese (Uni of Kaiserslautern), Shujian Liao (UCL), Baoren Xiao(UCL).


4 June 2021

Speaker: Abolfazl Safikhani (University of Florida)

Title and Abstract to Follow


18 June 2021 - 3-4pm UK time

Speaker: Runmin Wang (Southern Methodist University)

Title and Abstract to Follow


2 July 2021 - 3-4pm UK time

Speaker: Kevin Lin (University of Pennsylvania)

Title and Abstract to Follow

To register your interest in accessing the StatScale Seminars, contact Dr Hyeyoung Maeng 


26th March 2021 - Hao Chen (UC Davis)

A universal event detection framework for neuropixels data



12th March 2021 - Sumanta Basu (Cornell University)

Learning Financial Networks with Graphical Models of Time Series Data

26th February 2021 - Eric Kolaczyk (Boston University)

How hard is it to work with a network `average'?

12th February 2021 - Matteo Barigozzi (Università di Bologna)
Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm

29th January 2021 - Holger Dette (Ruhr-Universitaet Bochum)

Testing Relevant Hypotheses in Functional Time Series via Self-Normalization

4th December 2020 - Priyanga Dilini Talagala (University of Moratuwa)

Anomaly Detection in Streaming Time Series Data



20th November 2020 - Florian Pein (University of Cambridge)

About the loss function for cross-validation in change-point regression



6th November 2020 - Alex Aue (UC Davis)

Random matrix theory aids statistical inference in high dimensions



23rd October 2020 - Yoav Zemel (University of Cambridge)

Probabilistic approximations to optimal transport



9th October 2020 - Solt Kovacs (ETH Zurich)

Optimistic search strategy: change point detection for large-scale data via adaptive logarithmic queries



17th July 2020 - Tobias Kley (University of Bristol)

A new approach for open-end sequential change point monitoring



3rd July, 2020 - Claudia Kirch (Otto-von-Guericke University)

Functional change point detection for fMRI data 



19th June, 2020 - Martin Tveten (Dept. of Mathematics, University of Oslo)

Scalable changepoint and anomaly detection in cross-correlated data



5th June, 2020 - Yudong Chen (University of Cambridge)

High-dimensional, multiscale online changepoint detection