Enhanced visualization of time series through higher fourier harmonics

Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. A new visualization technique, the first Fourier harmonic projection (FFHP) was introduced to translate the multi-dimensional data into a two dimensional scatter plot where the spatial relationship of the points reflects the structure of the original data set. FFHP has been shown capable of visualizing various gene expression data sets. However, dimension arrangement is crucial for the effectiveness of FFHP and certain data, such as time series, prevents dimensions (time points) from being freely rearranged. In this paper, we present an alternative approach through higher Fourier harmonic projections to enhance the visualization. Our algorithm takes advantage of the theoretical meaning of the Fourier harmonics and "reshuffles" the dimensions of the data set without physically rearranging them. The experimental results demonstrated significant improvement of the visualizations.

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