1,720,993 research outputs found
Supplement for "Contextual Time Series Change Detection"
No full textTime series data are common in a variety of fields ranging from economics to medicine and manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin. (2013). Supplement for "Contextual Time Series Change Detection". Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215905
Tripoles: A New Class of Climate Teleconnections
No full textTeleconnections in climate represent a persistent and large-scale temporal
connection in a given climate variable between two distant geographical regions.
They are known to impact and explain the variability in climate of many regions
across the globe and have been a subject of interest to climatologists. Traditionally,
climate teleconnections have been studied as a persistent relationship between a pair
of geographical regions (e.g. North Atlantic Oscillation (NAO), and El-Nino Southern
Oscillation (ENSO)). In this report, we define a new class of climate teleconnections
which we refer to as tripoles that capture climatic relationships between three regions,
in contrast to teleconnections that are traditionally defined using only two regions.
We further provide a categorization of tripoles based on pairwise relationships between
the three participating regions and propose a shared nearest neighbor (SNN) graph-based
approach to find tripoles in a given spatio-temporal dataset.Agrawal, Saurabh; Atluri, Gowtham; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin. (2015). Tripoles: A New Class of Climate Teleconnections. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215985
Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience
No full textIn many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of time series that have strong linear dependence among themselves, with the requirement that each time series makes a significant contribution to the linear dependence. We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network. Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach. Using our proposed framework, we demonstrate the utility of multipoles in discovering new physical phenomena in two scientific domains: climate science and neuroscience. In particular, we discovered several multipole relationships that are reproducible in multiple other independent datasets, and lead to novel domain insights.Agrawal, Saurabh; Steinbach, Michael; Boley, Daniel; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin; Atluri, Gowtham. (2018). Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/216019
Using Temporal Detrending to Observe the Spatial Correlation of Traffic
No full textThis empirical study sheds light on the correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the correlation between 140 freeway traffic links in a sub-network of the Minneapolis - St. Paul highway system with a grid-like network topology. This topology enables us to juxtapose positive correlation with negative correlation, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective to the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted correlation structure can augment the accuracy of short-term traffic forecasting models.University of Minnesota RP Braun/CTS ChairErmagun, Alireza; Levinson, David M; Chatterjee, Snigdhansu. (2016). Using Temporal Detrending to Observe the Spatial Correlation of Traffic. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/181537
Contextual Time Series Change Detection
No full textTime series are commonly used in a variety of fields, ranging from economics to manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin. (2012). Contextual Time Series Change Detection. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215896
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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