For high-dimensional time series data, using line plots for integrated visualization can be messy. Thus, for quick visual mining of large multivariate time series datasets, a heatmap can be useful, showing time (x) versus the intensity of each variable of the multivariate time series data.
The script multivariate_time_series_visualization.py reads exemplary multivariate time series data (i.e., the Ozone Level Detection Data Set (Zhang et al.) from the UCI Machine Learning Repository) and generates a heatmap. Each variable is scaled into the range (0,1) for visualization purposes.
Moreover, such visualizations are useful to visually assess the reflectance captured at a given location by satellite-based remote sensing platforms such as the Landsat sensors. For example, the figure below shows the variation of top-of-atmosphere reflectance at different spectral bands (and band ratios NDVI and NDBI) captured betweeh 1984 and 2016 by the Landsat 5,7, and 8 sensors, at 4 different locations. Such a spectral-temporal reflectance plot illustrates seasonal effects and reflects certain land cover changes at the locations under study.
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Ozone Level Detection Data Set (Zhang et al.): https://archive.ics.uci.edu/ml/datasets/ozone+level+detection