4 results found
Lubba CH, Sethi SS, Knaute P, et al., catch22: CAnonical Time-series CHaracteristics, Data Mining and Knowledge Discovery, ISSN: 1384-5810
<jats:p>Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147\,000 time series, including biomedical datasets) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.</jats:p>
Sethi S, Ewers R, Jones N, et al., 2018, Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device, Methods in Ecology and Evolution, Vol: 9, Pages: 2383-2387, ISSN: 2041-210X
1. Automated methods of monitoring ecosystems provide a cost-effective way to track changes in natural system's dynamics across temporal and spatial scales. However, methods of recording and storing data captured from the field still require significant manual effort. 2. Here we introduce an open source, inexpensive, fully autonomous ecosystem monitoring unit for capturing and remotely transmitting continuous data streams from field sites over long time-periods. We provide a modular software framework for deploying various sensors, together with implementations to demonstrate proof of concept for continuous audio monitoring and time-lapse photography. 3. We show how our system can outperform comparable technologies for fractions of the cost, provided a local mobile network link is available. The system is robust to unreliable network signals and has been shown to function in extreme environmental conditions, such as in the tropical rainforests of Sabah, Borneo. 4. We provide full details on how to assemble the hardware, and the open-source software. Paired with appropriate automated analysis techniques, this system could provide spatially dense, near real-time, continuous insights into ecosystem and biodiversity dynamics at a low cost.
Sethi SS, Zerbi V, Wenderoth N, et al., 2017, Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain, Chaos, Vol: 27, ISSN: 1054-1500
Brain dynamics are thought to unfold on a network determined by the pattern of axonalconnections linking pairs of neuronal elements; the so-called connectome. Prior work has indicatedthat structural brain connectivity constrains pairwise correlations of brain dynamics(“functional connectivity”), but it is not known whether inter-regional axonal connectivity isrelated to the intrinsic dynamics of individual brain areas. Here we investigate this relationshipusing a weighted, directed mesoscale mouse connectome from the Allen Mouse BrainConnectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184brain regions in eighteen anesthetized mice. For each brain region, we measured degree,betweenness, and clustering coefficient from weighted and unweighted, and directed and undirectedversions of the connectome. We then characterized the univariate rs-fMRI dynamics ineach brain region by computing 6930 time-series properties using the time-series analysis toolbox,hctsa. After correcting for regional volume variations, strong and robust correlationsbetween structural connectivity properties and rs-fMRI dynamics were found only when edgeweights were accounted for, and were associated with variations in the autocorrelation propertiesof the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which waspositively correlated to the autocorrelation of fMRI time series at time lag s ¼ 34 s (partialSpearman correlation q ¼ 0:58), as well as a range of related measures such as relative high frequencypower (f > 0.4 Hz: q ¼ 0:43). Our results indicate that the topology of inter-regionalaxonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics suchthat regions with a greater aggregate strength of incoming projections display longer timescalesof activity fluctuations.
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