On-line motif detection in time series with SwiftMotif

Fuchs, Ernst; Gruber, Thiemo; Nitschke, Jiri; Sick, Bernhard

doi:10.1016/j.patcog.2009.05.004

Cited by 48 publications

(22 citation statements)

References 30 publications

(46 reference statements)

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“…In [14], [15] we have shown that a 0 , a 1 , a 2 , a 3 , etc. can be interpreted as the optimal estimators of average, slope, curve, change of curve, etc.…”

Section: Novel Features Describing Trends In Time Seriesmentioning

confidence: 97%

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All for one or one for all? Combining heterogeneous features for activity spotting

Blanke

Schiele

Kreil

et al. 2010

2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work are used. While feature selection algorithms allow automatic decision, definition of features remains a manual task. We conduct a comparative study of features with very different origin. To this end, we propose a new type of features based on polynomial approximation of signals. The new feature type is compared to features used routinely for motion based activity recognition as well as to recently proposed body-model based features. Experiments were performed on three different, large datasets allowing a thorough, in-depth analysis. They not only show the respective strengths of the different feature types but also their complementarity resulting in improved performance through combination. It shows that each feature type with its individual and complementary strengths and weaknesses can improve results by combination.

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“…In [14], [15] we have shown that a 0 , a 1 , a 2 , a 3 , etc. can be interpreted as the optimal estimators of average, slope, curve, change of curve, etc.…”

Section: Novel Features Describing Trends In Time Seriesmentioning

confidence: 97%

“…This makes this technique well-suited for time-critical applications. More details can be found in [14], [13].…”

Section: Novel Features Describing Trends In Time Seriesmentioning

confidence: 99%

All for one or one for all? Combining heterogeneous features for activity spotting

Blanke

Schiele

Kreil

et al. 2010

2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

Self Cite

View full text Add to dashboard Cite

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“…Particularly, a new methodology to detect on-line motifs in time series by combining probabilistic models and polynomial least-squares approximations was proposed in Fuchs et al (2009). This topic was also studied in Mueen and Keogh (2010) in which the authors found and maintained time series motifs from robotics, online compression and wildlife management domains.…”

Section: Motifs Discoverymentioning

confidence: 99%

Discovery of motifs to forecast outlier occurrence in time series

Martínez‐Álvarez

Troncoso

Riquelme

et al. 2011

Pattern Recognition Letters

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a b s t r a c tThe forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted.

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“…We refer to our previous work published in [16,17] which is based on mathematical background outlined in [18,19].…”

Section: Query Generationmentioning

confidence: 99%

Generating Shifting Workloads to Benchmark Adaptability in Relational Database Systems

Rabl¹,

Lang²,

Hackl³

et al. 2009

Lecture Notes in Computer Science

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Abstract. A large body of research concerns the adaptability of database systems. Many commercial systems already contain autonomic processes that adapt configurations as well as data structures and data organization. Yet there is virtually no possibility for a just measurement of the quality of such optimizations. While standard benchmarks have been developed that simulate real-world database applications very precisely, none of them considers variations in workloads produced by human factors. Today's benchmarks test the performance of database systems by measuring peak performance on homogeneous request streams. Nevertheless, in systems with user interaction access patterns are constantly shifting. We present a benchmark that simulates a web information system with interaction of large user groups. It is based on the analysis of a real online eLearning management system with 15,000 users. The benchmark considers the temporal dependency of user interaction. Main focus is to measure the adaptability of a database management system according to shifting workloads. We will give details on our design approach that uses sophisticated pattern analysis and data mining techniques.

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On-line motif detection in time series with SwiftMotif

Cited by 48 publications

References 30 publications

All for one or one for all? Combining heterogeneous features for activity spotting

All for one or one for all? Combining heterogeneous features for activity spotting

Discovery of motifs to forecast outlier occurrence in time series

Generating Shifting Workloads to Benchmark Adaptability in Relational Database Systems

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