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The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code
Implications of Z-normalization in the matrix profile
Companies are increasingly measuring their products and services, resulting in a rising amount of available time series data, making techniques to extract usable information needed. One state-of-the-art technique for time series is the Matrix Profile, which has been used for various applications including motif/discord discovery, visualizations and semantic segmentation. Internally, the Matrix Profile utilizes the z-normalized Euclidean distance to compare the shape of subsequences between two series. However, when comparing subsequences that are relatively flat and contain noise, the resulting distance is high despite the visual similarity of these subsequences. This property violates some of the assumptions made by Matrix Profile based techniques, resulting in worse performance when series contain flat and noisy subsequences. By studying the properties of the z-normalized Euclidean distance, we derived a method to eliminate this effect requiring only an estimate of the standard deviation of the noise. In this paper we describe various practical properties of the z-normalized Euclidean distance and show how these can be used to correct the performance of Matrix Profile related techniques. We demonstrate our techniques using anomaly detection using a Yahoo! Webscope anomaly dataset, semantic segmentation on the PAMAP2 activity dataset and for data visualization on a UCI activity dataset, all containing real-world data, and obtain overall better results after applying our technique. Our technique is a straightforward extension of the distance calculation in the Matrix Profile and will benefit any derived technique dealing with time series containing flat and noisy subsequences
Finding Motif Sets in Time Series
Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is to detect motif sets in household electricity-usage profiles, representing repeated patterns of household usage. We propose three algorithms for finding motif sets. Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly. We compare these algorithms on simulated datasets and on electricity-usage data. We show that Scan MK, the simplest way of using the best-matching pair to find motif sets, is less accurate on our synthetic data than Set Finder and Cluster MK, although the latter is very sensitive to parameter settings. We qualitatively analyse the outputs for the electricity-usage data and demonstrate that both Scan MK and Set Finder can discover useful motif sets in such data
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
Motifs are a powerful tool for analyzing physiological waveform data.
Standard motif methods, however, ignore important contextual information (e.g.,
what the patient was doing at the time the data were collected). We hypothesize
that these additional contextual data could increase the utility of motifs.
Thus, we propose an extension to motifs, contextual motifs, that incorporates
context. Recognizing that, oftentimes, context may be unobserved or
unavailable, we focus on methods to jointly infer motifs and context. Applied
to both simulated and real physiological data, our proposed approach improves
upon existing motif methods in terms of the discriminative utility of the
discovered motifs. In particular, we discovered contextual motifs in continuous
glucose monitor (CGM) data collected from patients with type 1 diabetes.
Compared to their contextless counterparts, these contextual motifs led to
better predictions of hypo- and hyperglycemic events. Our results suggest that
even when inferred, context is useful in both a long- and short-term prediction
horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1
Efficient Algorithms for the Closest Pair Problem and Applications
The closest pair problem (CPP) is one of the well studied and fundamental
problems in computing. Given a set of points in a metric space, the problem is
to identify the pair of closest points. Another closely related problem is the
fixed radius nearest neighbors problem (FRNNP). Given a set of points and a
radius , the problem is, for every input point , to identify all the
other input points that are within a distance of from . A naive
deterministic algorithm can solve these problems in quadratic time. CPP as well
as FRNNP play a vital role in computational biology, computational finance,
share market analysis, weather prediction, entomology, electro cardiograph,
N-body simulations, molecular simulations, etc. As a result, any improvements
made in solving CPP and FRNNP will have immediate implications for the solution
of numerous problems in these domains. We live in an era of big data and
processing these data take large amounts of time. Speeding up data processing
algorithms is thus much more essential now than ever before. In this paper we
present algorithms for CPP and FRNNP that improve (in theory and/or practice)
the best-known algorithms reported in the literature for CPP and FRNNP. These
algorithms also improve the best-known algorithms for related applications
including time series motif mining and the two locus problem in Genome Wide
Association Studies (GWAS)
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