1/3/2023 0 Comments Impact client 1.11![]() ![]() Below, we demonstrate how manipulating the window size can have little impact on your resulting matrix profile by running stump with varying windows sizes. Different Window Sizes #Īs we had mentioned above, stump should be robust to the choice of the window size parameter, m. We found that the top three peaks happened to correspond exactly with the timing of Columbus Day, Daylight Saving Time, and Thanksgiving, respectively. So what about the highest matrix profile values (filled triangles)? The subsequences that have the highest (local) values really emphasizes their uniqueness. Interestingly, the two lowest data points are exactly 7 days apart, which suggests that, in this dataset, there may be a periodicity of seven days in addition to the more obvious periodicity of one day. The lowest values (open triangles) are considered a motif since they represent the pair of nearest neighbor subsequences with the smallest z-normalized Euclidean distance. The data feature that we are interested in is the output steam flow telemetry that has units of kg/s and the data is “sampled” every three seconds with a total of 9,600 datapoints. #Impact client 1.11 generatorThis data was generated using fuzzy models applied to mimic a steam generator at the Abbott Power Plant in Champaign, IL. Let’s look at an example: Loading the Steamgen Dataset # #Impact client 1.11 seriesLuckily, the stump function takes in any time series (with floating point values) and computes the matrix profile along with the matrix profile indices and, in turn, one can immediately find time series motifs. The index to each nearest neighbor within the time series is referred to as the matrix profile index. This nearest neighbor distance vector is referred to as the matrix profile and In the case of STUMPY, all subsequences within a time series can be compared by computing the pairwise z-normalized Euclidean distances and then storing only the index to its nearest neighbor. Being able to say that a subsequence is “approximately repeated” requires that you be able to compare subsequences to each other. Time series motifs are approximately repeated subsequences found within a longer time series. Import pandas as pd import stumpy import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as dates from matplotlib.patches import Rectangle import datetime as dt plt. ![]()
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