Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

Anomaly Detection on Graph Time Series

In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional network. In order to incorporate external factors, we use feature extractor to augment the transition of latent variables, which can learn the influence of external factors. With the target function as accumulative ELBO, it is easy to extend this model to on-line method. The experimental study on traffic flow data shows the detection capability of the proposed method

Time-Dependent Models for the Afterglows of Massive Black Hole Mergers

The Laser Interferometer Space Antenna (LISA) will detect gravitational wave signals from coalescing pairs of massive black holes in the total mass range (10^5 - 10^7)/Msol out to cosmological distances. Identifying and monitoring the electromagnetic counterparts of these events would enable cosmological studies and offer new probes of gas physics around well-characterized massive black holes. Milosavljevic & Phinney (2005) proposed that a circumbinary disk around a binary of mass ~10^6 Msol will emit an accretion-powered X-ray afterglow approximately one decade after the gravitational wave event. We revisit this scenario by using Green's function solutions to calculate the temporal viscous evolution and the corresponding electromagnetic signature of the circumbinary disk. Our calculations suggest that an electromagnetic counterpart may become observable as a rapidly brightening source soon after the merger, i.e. several years earlier than previously thought. The afterglow can reach super-Eddington luminosities without violating the local Eddington flux limit. It is emitted in the soft X-ray by the innermost circumbinary disk, but it may be partially reprocessed at optical and infrared frequencies. We also find that the spreading disk becomes increasingly geometrically thick close to the central object as it evolves, indicating that the innermost flow could become advective and radiatively inefficient, and generate a powerful outflow. We conclude that the mergers of massive black holes detected by LISA offer unique opportunities for monitoring on humanly tractable timescales the viscous evolution of accretion flows and the emergence of outflows around massive black holes with precisely known masses, spins and orientations.Comment: 47 pages, 8 figures. v3 has been accepted to ApJ