A data mining algorithm for automated characterisation of fluctuations in multichannel timeseries

Abstract

We present a data mining technique for the analysis of multichannel oscillatory timeseries data and show an application using poloidal arrays of magnetic sensors installed in the H-1 heliac. The procedure is highly automated, and scales well to large datasets. The timeseries data is split into short time segments to provide time resolution, and each segment is represented by a singular value decomposition (SVD). By comparing power spectra of the temporal singular vectors, related singular values are grouped into subsets which define fluctuation structures. Thresholds for the normalised energy of the fluctuation structure and the normalised entropy of the SVD can be used to filter the dataset. We assume that distinct classes of fluctuations are localised in the space of phase differences Δ ψ (n, n + 1) between each pair of nearest neighbour channels. An expectation maximisation clustering algorithm is used to locate the distinct classes of fluctuations and assign mode numbers where possible, and a cluster tree mapping is used to visualise the results.