Browsing by Author "Agrawal, Saurabh"

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    Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience
    (2018-02-12) Agrawal, Saurabh; Steinbach, Michael; Boley, Daniel; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin; Atluri, Gowtham
    In 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.
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    Introducing Novel Relationships in Time Series Data
    (2018-12) Agrawal, Saurabh
    In many scientific and engineering domains such as climate, neuroscience, transportation, etc. measurements are collected from sensors installed in different parts of a complex dynamical system over regular intervals of time, resulting in a collection of large volumes of time series data. Automated data-driven approaches that can mine relationships between different time series could potentially lead to discovery of previously unknown physical processes which could further aid in designing policies and solutions to critical problems such as climate change, severe mental disorders, traffic congestion etc. This thesis defines novel relationships and patterns that can be studied in the time series data. In particular, the proposed definitions can capture two new types of relationships: i) multivariate relationships involving more than two time series, and ii) sub-interval relationships, that only exist during certain sub-intervals of time and are absent or occur very feebly during rest of the time. The other major contributions of this thesis include designing new automated data-driven approaches to find most interesting instances of defined relationships from the data in a computationally efficient manner, and proposing empirical approaches to assess the statistical significance of obtained relationships. The proposed approaches were applied to real-world datasets from two scientific domains: i) climate, and ii) neuroscience, and led to discovery of several new instances of relationships. Many of these instances are found to be statistically significant and reproducible in multiple time series datasets that are independent of the original dataset. One such instance led to the discovery of a climate phenomenon that was previously unknown to climate scientists.
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    Tripoles: A New Class of Climate Teleconnections
    (2015-12-11) Agrawal, Saurabh; Atluri, Gowtham; Liess, Stefan; Chatterjee, Snigdhansu; Kumar, Vipin
    Teleconnections 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.