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) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Time-series CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. 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.

Passive acoustic monitoring (PAM) has emerged as a transformative tool for applied ecology, conservation and biodiversity monitoring, but its potential contribution to fundamental ecology is less often discussed, and fundamental PAM studies tend to be descriptive, rather than mechanistic. Here, we chart the most promising directions for ecologists wishing to use the suite of currently available acoustic methods to address long‐standing fundamental questions in ecology and explore new avenues of research. In both terrestrial and aquatic habitats, PAM provides an opportunity to ask questions across multiple spatial scales and at fine temporal resolution, and to capture phenomena or species that are difficult to observe. In combination with traditional approaches to data collection, PAM could release ecologists from myriad limitations that have, at times, precluded mechanistic understanding. We discuss several case studies to demonstrate the potential contribution of PAM to biodiversity estimation, population trend analysis, assessing climate change impacts on phenology and distribution, and understanding disturbance and recovery dynamics. We also highlight what is on the horizon for PAM, in terms of near‐future technological and methodological developments that have the potential to provide advances in coming years. Overall, we illustrate how ecologists can harness the power of PAM to address fundamental ecological questions in an era of ecology no longer characterised by data limitation. Read the free Plain Language Summary for this article on the Journal blog.

Significance: Human pressures are causing natural ecosystems to change at an unprecedented rate. Understanding these changes is important (e.g., to inform policy decisions), but we are hampered by the slow, labor-intensive nature of traditional ecological surveys. In this study, we show that automated analysis of the sounds of an ecosystem—its soundscape—enables rapid and scalable ecological monitoring. We used a neural network to calculate fingerprints of soundscapes from a variety of ecosystems. From these acoustic fingerprints we could accurately predict habitat quality and biodiversity across multiple scales and automatically identify anomalous sounds such as gunshots and chainsaws. Crucially, our approach generalized well across ecosystems, offering promise as a backbone technology for global monitoring efforts.

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. 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. 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. 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.

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics (“functional connectivity”), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag τ = 34 s (partial Spearman correlation $\rho =0.58$ ), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: $\rho =-0.43$ ). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys.