Damien Fay from HPE visits COER to present at the Seminar Series
We wanted to thank Damien Fay from HPE for presenting at our 2024 Seminar Series yesterday. Demian gave an interesting presentation: "A self-organizing eigenspace map for time series clustering"
His talk focused on a time series clustering technique they developed as well as the underlying time series analysis techniques used which can handle non-stationary chaotic and semi-seasonal time series. He spent some time during the talk giving a tutorial on this before launching into the research which extends the theory substantially (see abstract below).
This paper presents a novel time series clustering method, the self-
organising eigenspace map (SOEM), based on a generalisation of the well-known self-organising feature map (SOFM). The SOEM operates on the eigenspaces of the embedded covariance structures of time series which are related directly to modes in those time series. Approximate joint diagonalisation acts as a pseudo-metric across these spaces allowing us to generalise the SOFM to a neural network with matrix input. The technique is empirically validated against three sets...








