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        "author": "Ameya Daigavane",
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        "description": "# TimeSeriesVisualization\nVisualization of Time-Series with the Matrix Profile and Multidimensional Scaling - a Python Implementation.\n\n## Introduction\nMultidimensional scaling is an algorithm that projects a set of objects represented by their distance matrix into a lesser dimensional space, such that the pairwise distance is preserved as much as possible. If we project onto a 1-dimensional or a 2-dimensional space, we can even plot the resultant projections, and visually understand the similarity between these objects.  \nAt a first glance, this seems applicable to time-series as well, where we want to capture similar regions across the entire time-series.  \nHowever, since we do not know apriori where our regions of interest are, we can simply select all subsequences of a given length from the original time-series.  \nLet's try this out!\n\n\n## References\n<b>Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series</b>.    \nChin-Chia Michael Yeh, Helga Van Herle, and Eamonn Keogh. IEEE ICDM 2016.\n\n\n",
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