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Significance of Patterns in Time Series Collections [chapter]

Niko Vuokko, Petteri Kaski
2011 Proceedings of the 2011 SIAM International Conference on Data Mining  
Wavelet-based methods have recently become the preferred way for significance testing of time series and time series collections, but these methods are still often based on fairly ad hoc bootstrapping  ...  Time series are a class of data whose complexity and rich structure make it difficult for data mining tools to extract meaningful patterns from them, and in particular to prune away the false positive  ...  null model for time series collections 2.1 General assumptions Suppose we are conducting significance testing on patterns in a collection X = {x i (t) | i = 1, 2, . . . , M } of homogeneous time series  ... 
doi:10.1137/1.9781611972818.58 dblp:conf/sdm/VuokkoK11 fatcat:heppbvxzmratdkjth5ccz6r5sa

A Methodological Framework for Characterizing the Spatiotemporal Variability of River Water-Quality Patterns Using Dynamic Factor Analysis

R. Aguilera
2016 Journal of Environmental Informatics  
Dynamic factor analysis (DFA) is used to extract underlying common patterns from sets of time-series with data gaps.  ...  We introduce a collection of methodological steps for the detection and characterization of the spatiotemporal variability of river water-quality patterns in the context of global environmental change.  ...  Long-term patterns extracted from sets of water quality time-series provide information on significant trends in a river basin.  ... 
doi:10.3808/jei.201600333 fatcat:iyi733uvo5crdadnwddmpxoj2m

Permutation test for periodicity in short time series data

Andrey A Ptitsyn, Sanjin Zvonic, Jeffrey M Gimble
2006 BMC Bioinformatics  
Identification of circadian expression pattern in time series data is important, but equally challenging.  ...  Results: To address this issue, we have developed a simple, but effective, computational technique for the identification of a periodic pattern in relatively short time series, typical for microarray studies  ...  This work was funded in part by the Pennington Biomedical Research Foundation (JMG).  ... 
doi:10.1186/1471-2105-7-s2-s10 pmid:17118131 pmcid:PMC1683571 fatcat:xhp7dqkglrfdvkqw7lqummsd5m

Permutation test for periodicity in short time series data

Andrey A Ptitsyn, Sanjin Zvonic, Jeffrey M Gimble
2007 BMC Bioinformatics  
Identification of circadian expression pattern in time series data is important, but equally challenging.  ...  Results: To address this issue, we have developed a simple, but effective, computational technique for the identification of a periodic pattern in relatively short time series, typical for microarray studies  ...  This work was funded in part by the Pennington Biomedical Research Foundation (JMG).  ... 
doi:10.1186/1471-2105-8-395 pmcid:PMC2098783 fatcat:7bnwit37trhu5pwxcwvbpzw6za

Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification [article]

Yadong Zhang, Xin Chen
2020 arXiv   pre-print
Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data.  ...  The areas in MDF with high weight in Grad-CAM have a high contribution from the significant motifs with the desired ordinal patterns associated with the signature patterns in time series.  ...  Acknowledgments Xin Chen acknowledges the financial support from the National Natural Science Foundation of China (Grant No. 21773182 (B030103) ).  ... 
arXiv:2001.07582v1 fatcat:aml3xtwjcrdjhnm2tjnlnxzlwm

Environmental influences on patterns of larval replenishment of coral reef fishes

DT Wilson, MG Meekan
2001 Marine Ecology Progress Series  
Time-series analysis detected significant correlations between catches of 8 species and wind direction; however, correlations were often weak and variable in direction.  ...  Firstly, time-series analyses were used to compare night-to-night patterns in light trap catches of 11 species (Astrapogon puncticulatus, Eucinostomus melanopterus, Lutjanus apodus, L. mahogani, Ophioblennius  ...  Data sets of catches and wind were concatenated into single time series prior to analysis. ns: not significant at 2 × SE. Lag in nights for correlation shown in parentheses.  ... 
doi:10.3354/meps222197 fatcat:zwfte2v3qnbdvop73424ys37a4

Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts

Fabon Dzogang, Thomas Lansdall-Welfare, Nello Cristianini
2016 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)  
Detection of collective Mood and Mental disorders We wish to study the relative attention devoted in a given time of the year to the expression of negative mood in Twitter and compare it to search patterns  ...  a small number of particularly emotive days within the series that describe reactions to individual events rather than the collective mood over time.  ... 
doi:10.1109/icdmw.2016.0136 dblp:conf/icdm/DzogangLC16 fatcat:6bo6hzbsj5dhxg3zpmdeqni4qe

Coefficient of cross correlation and the time domain correspondence

Li Li, Graham E. Caldwell
1999 Journal of Electromyography & Kinesiology  
In contrast, calculation of cross correlation for incremental phase shifts permits the identification of a maximal value that is an objective measure of the actual phase shifting between the two time series  ...  In this technical note, cross correlation and the 95% confidence interval of its maximum value are proposed as an objective means of pattern recognition and comparison.  ...  In addition to traditional methods of time series pattern identification, the coefficient of the cross correlation can be used to determine phase shift based on the entire profile of the time series without  ... 
doi:10.1016/s1050-6411(99)00012-7 pmid:10597051 fatcat:yyan7imrpfaunhaexbdg4yi5jq

Page 19 of Journal of Research on Adolescence Vol. 22, Issue 1 [page]

2012 Journal of Research on Adolescence  
In the first cluster of statistical analyses, descriptive statistics and a series of generalized linear analyses were used to characterize the pattern and potential significance of missing data.  ...  Patterns of missing data were examined both across and within the three waves of data collection.  ... 

Page 434 of The Journal of Animal Ecology Vol. 56, Issue 2 [page]

1987 The Journal of Animal Ecology  
The data for C. quinquefasciatus collected in Dubai (Table 1) were used for comparison with simulated data since this data series was both long and collected under stable climatic  ...  The CC of the parous and total time series has diminishing peaks at positive and negative multiples of the 4-day cycle.  ... 

Sampling-standardized expansion and collapse of reef building in the Phanerozoic

W. Kiessling
2008 Fossil Record  
Raw compilations of reef abundance per unit of time do not necessarily depict biologically meaningful patterns, because the waxing and waning of reefs might just follow the quality of the fossil record  ...  The sampling-standardized peaks in reef growth are essentially identical to those of previous studies, but significant peaks are rare.  ...  Figure 2 .Figure 3 . 23 Time series of collection counts in 49 Phanerozoic intervals.  ... 
doi:10.5194/fr-11-7-2008 fatcat:pgk6dufph5amhnqu6quq6xryfm

Analysing Mood Patterns in the United Kingdom through Twitter Content [article]

Vasileios Lampos, Thomas Lansdall-Welfare, Ricardo Araya, Nello Cristianini
2013 arXiv   pre-print
Our analysis results in the detection of strong and statistically significant circadian patterns for all the investigated mood types.  ...  In this work, we estimate temporal patterns of mood variation through the use of emotionally loaded words contained in Twitter messages, possibly reflecting underlying circadian and seasonal rhythms in  ...  Acknowledgments This work has been supported by the EU-FP7 project 'COMPLACS' and the PASCAL2 Network of Excellence.  ... 
arXiv:1304.5507v1 fatcat:op2lb2bvvvfqhbf2g7dw4c6zay

Detection of heat and cold waves in Montevergine time series (1884–2015)

Vincenzo Capozzi, Giorgio Budillon
2017 Advances in Geosciences  
series collected in Montevergine observatory.  ...  In this work, we have investigated about the frequency, the duration, the severity and the intensity of heat and cold waves in a Southern Italy high-altitude region, by analysing the climatological time  ...  The authors are also grateful to the Benedectine Community of Montevergine Abbey for affording the opportunity to use the original meteorological data collected in Montevergine Observatory.  ... 
doi:10.5194/adgeo-44-35-2017 fatcat:bietetitw5gv3n2rfidrppibsy

Interaction between Penaeid Shrimp and Fish Populations in the Gulf of Mexico: Importance of Shrimp as Forage Species

Masami Fujiwara, Can Zhou, Chelsea Acres, Fernando Martinez-Andrade, Chih-hao Hsieh
2016 PLoS ONE  
Co-integration is a robust method when analyzing non-stationary time series, and a majority of time series in this study was non-stationary.  ...  This may reflect the high statistical error rates inherent to the analysis of short nonstationary time series.  ...  Co-integration can find associations among time series without identifying the source of a non-stationary pattern; this is advantageous in population time series analysis because such a pattern could be  ... 
doi:10.1371/journal.pone.0166479 pmid:27832213 pmcid:PMC5104333 fatcat:ycv6ysgcljgrvanr4l2zk7xkla

ARIMA Model-Based Web Services Trustworthiness Evaluation and Prediction [chapter]

Meng Li, Zhebang Hua, Junfeng Zhao, Yanzhen Zou, Bing Xie
2012 Lecture Notes in Computer Science  
Then, the cumulative trustworthiness evaluation records are modeled as time series.  ...  As most Web services are delivered by third parties over unreliable Internet and are late bound at run-time, it is reasonable and useful to evaluate and predict the trustworthiness of Web services.  ...  If the series is a white noise series, the ACF for any lag is always zero. (4) Pattern Identification: There are several specific patterns for ARMA model (shown in Table 1 ).  ... 
doi:10.1007/978-3-642-34321-6_51 fatcat:eepplzavt5bpxeiwcatvvtni2e
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