StatScale Seminars

STATSCALE SEMINARS

CURRENT SEMINARS

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