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32
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
 SIGKDD'02
, 2002
"... ... mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in ..."
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Cited by 220 (50 self)
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... mining time series data. Literally hundreds of papers have introduced new algorithms to index, classify, cluster and segment time series. In this work we make the following claim. Much of this work has very little utility because the contribution made (speed in the case of indexing, accuracy in the case of classification and clustering, model accuracy in the case of segmentation) offer an amount of "improvement" that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets, or the variance that would have been observed by changing minor (unstated) implementation details. To illustrate our point
Discovering similar multidimensional trajectories
 In ICDE
, 2002
"... We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize nonmetric similarity functions based on the Longest C ..."
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Cited by 172 (6 self)
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We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize nonmetric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach. 1
Finding Surprising Patterns in a Time Series Database in Linear Time and Space
 In In proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2002
"... The problem of finding a specified pattern in a time series database (i.e. query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requir ..."
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Cited by 95 (6 self)
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The problem of finding a specified pattern in a time series database (i.e. query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of "surprise", and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these lim itations we introduce a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data. This notion has the advantage of not requiring an explicit definition of surprise, which may be impossible to elicit from a domain expert. Instead the user simply gives the algorithm a collection of previously observed normal data. Our algorithm uses a suffix tree to efficiently encode the frequency of all observed patterns and allows a Markov model to predict the expected frequency of previously unobserved patterns. Once the suffix tree has been constructed, a measure of surprise for all the patterns in a new database can be determined in time and space linear in the size of the database. We demonstrate the utility of our approach with an extensive experimental evaluation.
Indexing large humanmotion databases
 In Proc. 30th VLDB Conf
, 2004
"... Datadriven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of ..."
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Cited by 44 (5 self)
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Datadriven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of such data for general use and reuse is not yet a solved problem. Many of the existing techniques dealing with editing motion rely on indexing for annotation, segmentation, and reordering of the data. Euclidean distance is inappropriate for solving these indexing problems because of the inherent variability found in human motion. The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis. A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis
Visually mining and monitoring massive time series
 In Proceedings of the 10 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2004
"... Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/nogo decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of ..."
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Cited by 37 (12 self)
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Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/nogo decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of dollars, not including the cost in morale and other more intangible detriments. The Aerospace Corporation is responsible for providing engineering assessments critical to the go/nogo decision for every Department of Defense space vehicle. These assessments are made by constantly monitoring streaming telemetry data in the hours before launch. We will introduce VizTree, a novel timeseries visualization tool to aid the Aerospace analysts who must make these engineering assessments. VizTree was developed at the University of California, Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry. The use of a single tool for both aspects of the task allows a natural and intuitive transfer of mined knowledge to the monitoring task. Our visualization approach works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with stateoftheart batch algorithms on several real and synthetic datasets.
Iterative deepening dynamic time warping for time series
 In Proc 2 nd SIAM International Conference on Data Mining
, 2002
"... Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time s ..."
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Cited by 27 (8 self)
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time series [7]. Debregeas and Hebrail [8]
Three Myths about Dynamic Time Warping Data
 Mining, in the Proceedings of SIAM International Conference on Data Mining (2005
"... The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a nonlinear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for va ..."
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Cited by 22 (10 self)
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The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a nonlinear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW’s great success, there are still several persistent “myths ” about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.
Fast retrieval of similar subsequences in long sequence databases
 In 3 rd IEEE Knowledge and Data Engineering Exchange Workshop
, 1999
"... shpark,dongwon,wwc¡ Although the Euclidean distance has been the most popular similarity measure in sequence databases, recent techniques prefer to use highcost distance functions such as the time warping distance and the editing distance for wider applicability. However, if these distance function ..."
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Cited by 20 (3 self)
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shpark,dongwon,wwc¡ Although the Euclidean distance has been the most popular similarity measure in sequence databases, recent techniques prefer to use highcost distance functions such as the time warping distance and the editing distance for wider applicability. However, if these distance functions are applied to the retrieval of similar subsequences, the number of subsequences to be inspected during the search is quadratic to the ¢ average length of data sequences. In this paper, we propose a novel subsequence matching scheme, called the aligned subsequence matching, where the number of subsequences to be compared with a query sequence is reduced to ¢ linear to. We also present an indexing technique to speedup the aligned subsequence matching using the similarity measure of the modified time warping distance. The experiments on the synthetic data sequences demonstrate the effectiveness of our proposed approach; ours consistently outperformed the sequential scanning and achieved up to 6.5 times speedup. 1.
SegmentBased Approach for Subsequence Searches in Sequence Databases
, 2001
"... This paper investigates the subsequence searching problem under time warping in sequence databases. Time warping enables to find sequences with similar changing patterns even when they are of different lengths. Our work is motivated by the observation that subsequence searches slow down quadraticall ..."
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Cited by 20 (0 self)
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This paper investigates the subsequence searching problem under time warping in sequence databases. Time warping enables to find sequences with similar changing patterns even when they are of different lengths. Our work is motivated by the observation that subsequence searches slow down quadratically as the total length of data sequences increases. To resolve this problem, we propose the SegmentBased Approach for Subsequence Searches (SBASS), which modifies the similarity measure from time warping to piecewise time warping and limits the number of possible subsequences to be compared with a query sequence. For efficient
Robust Similarity Measures for Mobile Object Trajectories
 Proc. of DEXA Workshops
, 2002
"... We investigate techniques for similarity analysis of spatiotemporal trajectories for mobile objects. Such kind of data may contain a great amount of outliers, which degrades the performance of Euclidean and Time Warping Distance. Therefore, here we propose the use of nonmetric distance functions b ..."
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Cited by 20 (1 self)
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We investigate techniques for similarity analysis of spatiotemporal trajectories for mobile objects. Such kind of data may contain a great amount of outliers, which degrades the performance of Euclidean and Time Warping Distance. Therefore, here we propose the use of nonmetric distance functions based on the Longest Common Subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various L p Norms and also to Time Warping distance (for real and synthetic data) and we present experimental results that validate the accuracy and efficiency of our approach, especially under the strong presence of noise.