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

Specific Relationship Between the Shape of the Readiness Potential, Subjective Decision Time, and Waiting Time Predicted by an Accumulator Model with Temporally Autocorrelated Input Noise

Self-initiated movements are reliably preceded by a gradual buildup of neuronal activity known as the readiness potential (RP). Recent evidence suggests that the RP may reflect subthreshold stochastic fluctuations in neural activity that can be modeled as a process of accumulation to bound. One element of accumulator models that has been largely overlooked in the literature is the stochastic term, which is traditionally modeled as Gaussian white noise. While there may be practical reasons for this choice, we have long known that noise in neural systems is not white – it is long-term correlated with spectral density of the form 1/f^β (with roughly 1 \u3c β \u3c 3) across a broad range of spatial scales. I explored the behavior of a leaky stochastic accumulator when the noise over which it accumulates is temporally autocorrelated. I also allowed for the possibility that the RP, as measured at the scalp, might reflect the input to the accumulator (i.e., its stochastic noise component) rather than its output. These two premises led to two novel predictions that I empirically confirmed on behavioral and electroencephalography data from human subjects performing a self-initiated movement task. In addition to generating these two predictions, the model also suggested biologically plausible levels of autocorrelation, consistent with the degree of autocorrelation in our empirical data and in prior reports. These results expose new perspectives for accumulator models by suggesting that the spectral properties of the stochastic input should be allowed to vary, consistent with the nature of biological neural noise

Evidence of a decadal solar signal in the Amazon River: 1903 to 2013

It has been shown that tropical climates can be notably influenced by the decadal solar cycle; however, the relationship between this solar forcing and the tropical Amazon River has been overlooked in previous research. In this study, we reveal evidence of such a link by analyzing a 1903-2013 record of Amazon discharge. We identify a decadal flow cycle that is anticorrelated with the solar activity measured by the decadal sunspot cycle. This relationship persists through time and appears to result from a solar influence on the tropical Atlantic Ocean. The amplitude of the decadal solar signal in flow is apparently modulated by the interdecadal North Atlantic variability. Because Amazonia is an important element of the planetary water cycle, our findings have implications for studies on global change.Fil: Antico, Andres. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Torres, Maria Eugenia. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

Stability of Monitoring Weak Changes in Multiply Scattering Media with Ambient Noise Correlation: Laboratory Experiments

Previous studies have shown that small changes can be monitored in a scattering medium by observing phase shifts in the coda. Passive monitoring of weak changes through ambient noise correlation has already been applied to seismology, acoustics and engineering. Usually, this is done under the assumption that a properly reconstructed Green function as well as stable background noise sources are necessary. In order to further develop this monitoring technique, a laboratory experiment was performed in the 2.5MHz range in a gel with scattering inclusions, comparing an active (pulse-echo) form of monitoring to a passive (correlation) one. Present results show that temperature changes in the medium can be observed even if the Green function (GF) of the medium is not reconstructed. Moreover, this article establishes that the GF reconstruction in the correlations is not a necessary condition: the only condition to monitoring with correlation (passive experiment) is the relative stability of the background noise structure

Applying and interpreting model-based seasonal adjustment : the Euro-area industrial production series

La crisis económica reciente ha alterado la dinámica de las series económicas y, como consecuencia, ha introducido incertidumbre en el ajuste estacional en estos últimos años. El problema se discutió en seminarios celebrados en Eurostat y en el Banco Central Europeo durante el año 2010, en el contexto de la desestacionalización del Índice de Producción Industrial del área del euro. Dado que un componente estacional nunca se observa como tal y ni siquiera está definido de forma precisa, es difícil comparar resultados de desestacionalizaciones distintas. Sin embargo, dentro del método basado en la extracción de señales en modelos del tipo regresión-ARIMA, existe un marco que permite un análisis sistemático y la comparación de resultados obtenidos con distintos modelos. Bajo el marco de TRAMO-SEATS, se analiza la serie de producción industrial mencionada. El propósito del análisis no es la comparación de métodos alternativos, sino mostrar cómo el marco modelístico y los resultados del análisis basado en modelos pueden ser utilizados en las etapas de identificación, diagnóstico e inferencia, y en la selección de un modelo y un ajuste estacional adecuado. A pesar de la incertidumbre provocada por la crisis (y de las revisiones en la serie original) el procedimiento automático, introduciendo rampas que capturan la caída espectacular que experimenta la serie en 2008, produce excelentes resultados, estables en el tiemp

Técnicas de modelado matemático paramétrico y no paramétrico: un caso práctico de identificación de una máquina eléctrica

El modelado matemático es una característica muy importante en relación con el análisis y control de sistemas dinámicos. Además, la identificación del sistema es un enfoque para construir expresiones matemáticas a partir de datos experimentales tomados de procesos. En este contexto, este trabajo describe varias técnicas de modelado e identificación que son herramientas poderosas para determinar el comportamiento de los sistemas dinámicos en el tiempo. En Este trabajo se enfatiza las principales ventajas y/o desventajas que tienen las diferentes formulaciones matemáticas de modelación e identificación. También se presenta una revisión exhaustiva de las principales técnicas de modelado e identificación desde una perspectiva paramétrica y no paramétrica. Se formularon los modelos paramétricos y no paramétricos por medio de sus ecuaciones para aplicarlos en un caso de estudio. Los datos experimentales se toman de una máquina eléctrica, un motor de DC de una plataforma didáctica en la cual se aplican un conjunto de entradas conocidas para medir la velocidad del motor y utilizar estos datos como parte del proceso de modelación e identificación. El artículo concluye con las soluciones proporcionadas por la comparación de técnicas de modelación e identificación donde soluciones sencillas como los sistemas de primer orden son precisos para modelar un motor DC de dinámica lineal sobre otras formulaciones matemáticas más complejasMathematical modeling is an important feature concerning the analysis and control of dynamic systems. Also, system identification is an approach for building mathematical expressions from experimental data taken from processes performance. In this context, the contemporaneous state of the art describes several modelling and identification techniques which are excellent alternatives to determine systems behavior through time. This paper presents a comprehensive review of the main techniques for modeling and identification from a parametric and no parametric perspective. Experimental data are taken from an electrical machine that is a DC motor from a didactic platform. The paper concludes with the analysis of results taken from different identification procedures

New Eurocoin: Tracking Economic Growth in Real Time

Removal of short-run dynamics from a stationary time series to isolate the medium to long-run component, can be obtained by a band-pass filter. However, band pass filters are infinite moving averages and can therefore deteriorate at the end of the sample. This is a well-known result in the literature isolating the business cycle in integrated series. We show that the same problem arises with our application to stationary time series. In this paper we develop a method to obtain smoothing of a stationary time series by using only contemporaneous values of a large dataset, so that no end-of-sample deterioration occurs. Our construction is based on a special version of Generalized Principal Components, which is designed to use leading variables in the dataset as proxies for missing future values in the variable of interest. Our method is applied to the construction of New Eurocoin, an indicator of economic activity for the euro area. New Eurocoin is an estimate, in real time, of the medium to long-run component of the euro area GDP growth, which performs equally well within and at the end of the sample. As our dataset is monthly and most of the series are updated with a short delay, we are able to produce a monthly, real-time indicator. An assessment of its performance as an approximation of the medium to long-run GDP growth, both in terms of fitting and turning-point signaling, is provided.Coincident Indicator, Band-pass Filter, Large-dataset Factor Models, Generalized Principal Components

Passive detection of moving aerial target based on multiple collaborative GPS satellites

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite

The COST IRACON Geometry-based Stochastic Channel Model for Vehicle-to-Vehicle Communication in Intersections

Vehicle-to-vehicle (V2V) wireless communications can improve traffic safety at road intersections and enable congestion avoidance. However, detailed knowledge about the wireless propagation channel is needed for the development and realistic assessment of V2V communication systems. We present a novel geometry-based stochastic MIMO channel model with support for frequencies in the band of 5.2-6.2 GHz. The model is based on extensive high-resolution measurements at different road intersections in the city of Berlin, Germany. We extend existing models, by including the effects of various obstructions, higher order interactions, and by introducing an angular gain function for the scatterers. Scatterer locations have been identified and mapped to measured multi-path trajectories using a measurement-based ray tracing method and a subsequent RANSAC algorithm. The developed model is parameterized, and using the measured propagation paths that have been mapped to scatterer locations, model parameters are estimated. The time variant power fading of individual multi-path components is found to be best modeled by a Gamma process with an exponential autocorrelation. The path coherence distance is estimated to be in the range of 0-2 m. The model is also validated against measurement data, showing that the developed model accurately captures the behavior of the measured channel gain, Doppler spread, and delay spread. This is also the case for intersections that have not been used when estimating model parameters.Comment: Submitted to IEEE Transactions on Vehicular Technolog