3 papers
stat.ME2022
A Multivariate Spatial and Spatiotemporal ARCH Model
Philipp Otto
This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spati…
cs.LG2020
Statistical learning for change point and anomaly detection in graphs
Anna Malinovskaya, Philipp Otto, Torben Peters
Complex systems which can be represented in the form of static and dynamic graphs arise in different fields, e.g. communication, engineering and industry. One of the interesting pr…
stat.ME2019
Spatial and Spatiotemporal GARCH Models -- A Unified Approach
Philipp Otto, Wolfgang Schmid
In time-series analyses, particularly for finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volat…