Visual Analytics for Large-scale Time Series
Multivariate time series are common in many sectors and fields such as finance, energy or biology. They are also often long and high-dimensional: As an example, 5 years of intraday stock data for 50 stocks contain 50 dimensions and >2.5M time points. This is too much data to display in its raw form, and often needs to be reduced in some way for visualization. Such approaches could e.g. be clustering of daily patterns, or applying dimension reduction algorithms to time slices.
Provide a systematic overview of how VA applications reduce, if so, and visualize very long or high-dimensional time series.
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