Moving Time Window Research Articles

Coherence between the fields of low-frequency seismic noise in Japan and California

The relationship is considered between the statistics of the field of low-frequency seismic noise which was synchronously recorded by two broadband seismic networks in Japan (78 stations) and California (81 stations). The analysis is based on the data for seven years of observations (2008–2014). For each network, the daily time series of the median values are constructed for five parameters of seismic noise: kurtosis (excess), minimal normalized entropy of the distribution of the squared wavelet coefficients, generalized Hurst exponent, support width of the singularity spectrum, and index of linear predictability. The median values for each parameter were calculated on a daily basis over all the stations of the networks and resulted in a time series containing 2557 data points of the integral characteristics of the noise with a daily time step. The use of the median values of the noise parameters avoids considering the effects of the gaps in recording by individual stations and provides the continuous time series as the integral characteristic of the whole network. Next, for each network, the aggregate signals were calculated for the obtained five-variate time series. By construction, the aggregate signal is a scalar signal which maximally accumulates the most general variations that are simultaneously present in all the analyzed signals and, at the same time, rejects the components that are only characteristic of a single process. The final step of the analysis consists in estimating the evolution of the quadratic spectrum in the moving time window with a length of one year. It is shown that during the considered interval of the observations, the coherence is characterized by the increasing linear trend, which independently supports the previous conclusion about the enhancement of the synchronization between the parameters of the global seismic noise.

Model reference composite learning control without persistency of excitation

Parameter convergence is desirable in adaptive control as it brings several attractive features, including accurate online modelling, exponential tracking, and robust adaptation without parameter drift. However, a strong persistent-excitation (PE) condition must be satisfied to guarantee parameter convergence in the conventional adaptive control. This study proposes a model reference composite learning control strategy to guarantee parameter convergence without the PE condition. In the composite learning, an integral at a moving-time window is applied to construct a prediction error, an integral transformation is derived for avoiding the time derivation of plant states in the calculation of the prediction error, and both the tracking error and the prediction error are applied to update parametric estimates. Global exponential stability of the closed-loop system is established under an interval-excitation condition which is much weaker than the PE condition. Compared with a concurrent learning technique that has the same aim as this study, the proposed composite learning technique avoids the usage of singular value maximisation and fixed-point smoothing resulting in a considerable reduction of computational cost. Numerical results have verified effectiveness and superiority of the proposed control strategy.

Long-range coherence between seismic noise properties in Japan and California before and after Tohoku mega-earthquake

The coherences between daily time series of four low-frequency seismic noise properties which were calculated for 78 broadband seismic stations of the network F-net in Japan and 81 broadband seismic stations in California for 13 years of observation, 2003–2015, is investigated. The studied time interval includes Tohoku mega-earthquake, M9, on March 11, 2011. The chosen noise properties are the following: minimum normalized entropy of squared wavelet coefficients, multifractal singularity spectrum support width, generalized Hurst exponent and index of linear predictability. These properties were estimated daily as median values taken over all stations of the networks. For each pair of these noise properties from Japan and California squared coherence spectrums were estimated within moving time window of the length 730 days. The maximum values of squared coherence spectra for periods more than 20 days were essentially increasing as the time window approaches the time moment of Tohoku mega-earthquake and achieved their maximum values for position of moving time window strictly before the seismic catastrophe. This fact is interpreted as a consequence of general global seismic noise synchronization before huge seismic catastrophe.

Improving precipitation forecast with hybrid 3DVar and time-lagged ensembles in a heavy rainfall event

This study evaluates the performance of three-dimensional variational (3DVar) and a hybrid data assimilation system using time-lagged ensembles in a heavy rainfall event. The time-lagged ensembles are constructed by sampling from a moving time window of 3h along a model trajectory, which is economical and easy to implement. The proposed hybrid data assimilation system introduces flow-dependent error covariance derived from time-lagged ensemble into variational cost function without significantly increasing computational cost. Single observation tests are performed to document characteristic of the hybrid system. The sensitivity of precipitation forecasts to ensemble covariance weight and localization scale is investigated. Additionally, the TLEn-Var is evaluated and compared to the ETKF(ensemble transformed Kalman filter)-based hybrid assimilation within a continuously cycling framework, through which new hybrid analyses are produced every 3h over 10days. The 24h accumulated precipitation, moisture, wind are analyzed between 3DVar and the hybrid assimilation using time-lagged ensembles.Results show that model states and precipitation forecast skill are improved by the hybrid assimilation using time-lagged ensembles compared with 3DVar. Simulation of the precipitable water and structure of the wind are also improved. Cyclonic wind increments are generated near the rainfall center, leading to an improved precipitation forecast. This study indicates that the hybrid data assimilation using time-lagged ensembles seems like a viable alternative or supplement in the complex models for some weather service agencies that have limited computing resources to conduct large size of ensembles.

Detection of occupancy profile based on carbon dioxide concentration pattern matching

Abstract Occupancy is one of major factors influencing indoor microclimate. The aim of this work was to determine the influence of this factor on indoor air quality (IAQ) on the basis of CO 2 concentration measurements and statistical analysis. We wanted to identify periods of time when IAQ was strongly affected by the occupancy described by the given profile. The proposed approach consisted of several stages. The CO 2 concentration was measured and recorded in the form of univariate time series. Then, the relationship between occupancy and internal structure of the time series was disclosed. There were distinguished segments based on sample periodogram. Each segment was associated with a particular occupancy profile. In order to detect how human factor represented by a given occupancy profile influences IAQ we proposed to use pattern matching. In this approach there was examined the similarity between segments of the time series and the pattern of CO 2 variability, which represented a selected occupancy profile. The analysis was performed in time domain using moving time window technique. The similarity was judged based on two types of indexes, namely correlation coefficients and distance measures. It was shown that our approach may be applied to successfully detect a particular occupancy profile. The best performance was achieved when using angular distance as the similarity index. In this case we reached 82% true positive and 22% false positive detections. The proposed method may be applied in diagnostics problems to reveal sources of indoor air quality problems.

Temporal association between childhood leukaemia and population growth in Swiss municipalities.

The population mixing hypothesis proposes that childhood leukaemia (CL) might be a rare complication of a yet unidentified subclinical infection. Large population influxes into previously isolated rural areas may foster localised epidemics of the postulated infection causing a subsequent increase of CL. While marked population growth after a period of stability was central to the formulation of the hypothesis and to the early studies on population mixing, there is a lack of objective criteria to define such growth patterns. We aimed to determine whether periods of marked population growth coincided with increases in the risk of CL in Swiss municipalities. We identified incident cases of CL aged 0-15years for the period 1985-2010 from the Swiss Childhood Cancer Registry. Annual data on population counts in Swiss municipalities were obtained for 1980-2010. As exposures, we defined (1) cumulative population growth during a 5-year moving time window centred on each year (1985-2010) and (2) periods of 'take-off growth' identified by segmented linear regression. We compared CL incidence across exposure categories using Poisson regression and tested for effect modification by degree of urbanisation. Our study included 1500 incident cases and 2561 municipalities. The incident rate ratio (IRR) comparing the highest to the lowest quintile of 5-year population growth was 1.18 (95% CI 0.96, 1.46) in all municipalities and 1.33 (95% CI 0.93, 1.92) in rural municipalities (p value interaction 0.36). In municipalities with take-off growth, the IRR comparing the take-off period (>6% annual population growth) with the initial period of low or negative growth (<2%) was 2.07 (95% CI 0.95, 4.51) overall and 2.99 (1.11, 8.05) in rural areas (p interaction 0.52). Our study provides further support for the population mixing hypothesis and underlines the need to distinguish take-off growth from other growth patterns in future research.

Zastosowanie równa�n ró�zniczkowo-algebraicznych do predykcji zmian parametrów powietrza wewn�etrznego

In this paper we attempt to apply differential-algebraic systems to predict changes of indoor air quality (IAQ). So far, differential-algebraic equations (DAEs) have not been used in this way. The indicators of the indoor air quality, which were examined are temperature, relative humidity and carbon dioxide concentration. In order to model changes of these parameters the system of differential-algebraic equations was proposed. The approach applied in this work utilized the method of moving time window with variable length (4, 8, 10 time points). In these time windows models were parameterized. The prediction horizon was in the range from 1 to 15 minutes. To assess prediction quality three measures were used: mean absolute error, maximum absolute error and root mean squared error.(The new prediction method for determining indoor air quality parameters changes based on differential-algebraic systems) Slowa kluczowe: ocena jakopowietrza wewn ˛ etrznego, rownania ro˙aiczne.

Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals.

This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian eigenmaps machine learning approach with dynamical systems ideas to analyze emergent dynamic behaviors. The method reconstructs the abstract dynamical system phase-space geometry of the embedded measurements and tracks changes in physiological conditions or activities through changes in that geometry. It is geared to extract information from the joint behavior of time traces obtained from large sensor arrays, such as those used in multiple-electrode ECG and EEG, and explore the geometrical structure of the low dimensional embedding of moving time windows of those joint snapshots. Our main contribution is a method for mapping vectors from the phase space to the data domain. We present cases to evaluate the methods, including a synthetic example using the chaotic Lorenz system, several sets of cardiac measurements from both canine and human hearts, and measurements from a human brain.

Simulation of High-Resolution Precipitable Water Data by a Stochastic Model with a Random Trigger

We use a stochastic differential equation (SDE) model with a random precipitation trigger for mass balance to simulate the 20 s temporal resolution column precipitable water vapor (PWV) data during the tropical warm pool international cloud experiment (TWP-ICE) period of January 20 to February 15, 2006 at Darwin, Australia. The trigger is determined by an exponential cumulative distribution function, the time step size in the SDE simulation, and a random precipitation indicator uniformly distributed over [0, 1]. Compared with the observed data, the simulations have similar means, extremes, skewness, kurtosis, and overall shapes of probability distribution, and are temporally well synchronized for increasing and decreasing, but have about 20% lower standard deviation. Based on a 1000-day run, the correlations between the model data and the observations in TWP-ICE period were computed in a moving time window of 25 days and show quasi-periodic variations between (−0.675, 0.697). This shows that the results are robust for the stochastic model simulation of the observed PWV data, whose fractal dimension is 1.9, while the dimension of the simulated data is also about 1.9. This agreement and numerous sensitivity experiments form a test on the feasibility of using an SDE model to simulate precipitation processes in more complex climate models.

Composite adaptive dynamic surface control using online recorded data

SummaryThis paper presents an online recorded data‐based design of composite adaptive dynamic surface control for a class of uncertain parameter strict‐feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed‐loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time‐interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach. Copyright © 2016 John Wiley &amp; Sons, Ltd.

EXPERIENCE OF COMPLEXATION OF GLOBAL GEOPHYSICAL OBSERVATIONS

The technique of joint analysis of heterogeneous time series of geophysical monitoring systems for the purpose of detecting time intervals and specific periods of bursts of synchronous behavior is presented. The technique is based on the use of the Fourier‐aggregated signals and spectral measures of coherent behavior of multivariate time series, estimated in moving time windows. The article presents results of the analysis of data of underground electrical surveys at stations located in Kamchatka, Altai and Italy; the data were analysed together with torsion pendulum movements in Tula (Russia) and the time series of seismic noise parameters at the Japanese islands for the interval 2012–2015. The analysis identified a number of significant bursts of coherent behavior for these observations, some of which are presumably due to the strong mantle Okhotsk Sea earthquake of 24 May 2013. The coherent behavior of various geophysical fields before and after strong earthquakes is interpreted as a manifestation of the general pattern of increasing synchronization of fluctuations of complex systems at their approach to the rapid changes in their properties.

Multivariate Statistical Kernel PCA for Nonlinear Process Fault Diagnosis in Military Barracks

Because of the nonlinear characteristics of monitoring system in military barracks, the traditional KPCA method either have low sensitivity or unable to detect the fault quickly and accurately. In order to make use of higher-order statistics to get more useful information and meet the requirements of real-time fault diagnosis and sensitivity, a new method of fault detection and diagnosis is proposed based on multivariate statistical kernel principal component analysis (MSKPCA), which combines statistic pattern analysis framework (SPA) and kernel principal component analysis (KPCA). First, the transformation of multivariate statistics and kernel function are conducted in which technology of moving time window is used. Then, PCA is executed to analysis the kernel function obtained from the first step. Moreover, the statistics of T^2 and SPE and the control limits of them are calculated. Finally, simulations on a typical nonlinear numerical example show that the proposed MSKPCA method is more effective than PCA and KPCA in terms of fault detection and diagnosis.

Joint probability analysis of extreme wave heights and storm surges in the Aegean Sea in a changing climate

Joint probability analysis is most often conducted within a stationary framework. In the present study a nonstationary bivariate approach is used to investigate the changes in the joint probabilities of extreme wave heights and corresponding storm surges with time. The dependence structure of the studied variables is modelled using copulas. The nonstationary Generalized Extreme Value (GEV) distribution is utilized to model the marginal distribution functions of the variables, within a 40-year moving time window. All parameters of the GEV are tested for statistically significant linear and polynomial trends over time. Then different copula functions are fitted to model the dependence structure of the data. The nonstationarity of the dependence structure of the studied variables is also investigated. The methods and techniques of the present work are implemented to wave height annual maxima and corresponding storm surges at two selected areas of the Aegean Sea. The analysis reveals the existence of trends in the joint exceedance probabilities of the variables, in the most likely events selected for each time interval, as well as in a defined hazard series, such as the water level at the coastline.

ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor.

Multi-attribute time-series data plays a vital role in many different domains, such as economics, sensor networks, and biology. An important task when making sense of such data is to provide users with an overview to identify items that show an interesting development over time, including both absolute and relative changes in multiple attributes simultaneously. However, this is not well supported by existing visualization techniques. To address this issue, we present ThermalPlot, a visualization technique that summarizes combinations of multiple attributes over time using an items position, the most salient visual variable. More precisely, the x-position in the ThermalPlot is based on a user-defined degree-of-interest (DoI) function that combines multiple attributes over time. The y-position is determined by the relative change in the DoI value ( ∆DoI) within a user-specified time window. Animating this mapping via a moving time window gives rise to circular movements of items over time-as in thermal systems. To help the user to identify important items that match user-defined temporal patterns and to increase the technique's scalability, we adapt the level of detail of the items' representation based on the DoI value. Furthermore, we present an interactive exploration environment for multi-attribute time-series data that ties together a carefully chosen set of visualizations, designed to support analysts in interacting with the ThermalPlot technique. We demonstrate the effectiveness of our technique by means of two usage scenarios that address the visual analysis of economic development data and of stock market data.

A New Scheme for Monitoring and Diagnosis of Multistage Manufacturing Processes Using Product Quality Measurements

&#x0D; &#x0D; &#x0D; The development of robust monitoring systems for assuring the consistency and stability of multistage manufacturing processes necessitates the use of add-on sensors and advanced data collection, storage, and analysis platforms to deal with the high-dimensional data collected from machines and products in multiple stages. In many cases, such an approach may not be feasible due to high implementation costs and the challenges of obtaining the process parameters and analyzing them effectively. This paper proposes an alternative approach for health monitoring and diagnosis of multistage manufacturing processes based on product quality measurements in a sensor-less environment. In the presented work, the available data consists of product quality parameters measured from multiple product types along with the manufacturing route associated with each product. A Gamma distribution is fit to the data for each parameter within a moving time window. Using the distribution fits, a metric is developed to represent the performance of each machine in a stage compared to its peers producing the same product. This metric is then aggregated across all the products produced by the machine to generate the final metric reflecting the overall performance of the machine. This performance metric is first calculated for the machines in the last stage. After flagging the underperforming machines in the last stage, the samples from those machines are removed from the data set and the remaining samples are used to calculate the similar metric for the prior stage. The suggested approach assumes the random distribution of products from one stage to the next to facilitate the implementation of a comparison-based approach. This approach is tested on a data set collected from a manufacturing plant. The results demonstrate the effectiveness of such approach for monitoring and diagnosis of multistage manufacturing processes when the data is not available from within the process.&#x0D; &#x0D; &#x0D;

A Comparison of Multiple-Imputation Methods for Handling Missing Data in Repeated Measurements Observational Studies

SummaryMultiple-imputation (MI) methods for imputing missing data in observational health studies with repeated measurements were evaluated with particular focus on incomplete time varying explanatory variables. Standard and random-effects imputation by chained equations, multivariate normal imputation and Bayesian MI were compared regarding bias and efficiency of regression coefficient estimates by using simulation studies. Flexibility of the methods in handling different types of variables (binary, categorical, skewed and normally distributed) and correlations between the repeated measurements of the incomplete variables were also compared. Multivariate normal imputation produced the least bias in most situations, is theoretically well justified and allows flexible correlation for the repeated measurements. It can be recommended for imputing continuous variables. Bayesian MI is efficient and may be preferable in the presence of categorical and non-normally distributed continuous variables. Imputation by chained equations approaches were sensitive to the correlation between the repeated measurements. The moving time window approach may be used for normally distributed continuous variables with auto-regressive correlation.

Precipitation Calibration Based on the Frequency-Matching Method

Abstract A postprocessing technique is employed to correct model bias for precipitation fields in real time based on a comparison of the frequency distributions of observed and forecast precipitation amounts. Essentially, a calibration is made by defining an adjustment to the forecast value in such a way that the adjusted cumulative forecast distribution over a moving time window dynamically matches the corresponding observed distribution accumulated over a domain of interest, for example, the entire conterminous United States (CONUS), or different River Forecast Center (RFC) regions in the cases examined herein. In particular, the Kalman filter method is used to catch the flow dependence and bias information. Calibration is done on a pointwise basis for a specified domain. Using this unique technique, the calibration of precipitation forecasts for the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) was implemented in May 2004. To further satisfy various users, a recent upgrade to the May 2004 implementation has been made for higher resolution with better analyses. From this study, it was found that this method has a positive impact on the intensity-dominated errors but has some common limitations with extreme events and dry bias elimination like other precipitation calibration methods. Overall, the frequency-matching algorithm substantially improves NCEP Global Forecast System (GFS) and GEFS systematic precipitation forecast errors (or biases) over a wide range of forecast amounts and produces more realistic precipitation patterns. Moreover, this approach improves the deterministic forecast skills measured by most verification scores through applying this method to GFS and GEFS ensemble means.

New Criteria to Assess Seismic and Rock Burst Hazard in Coal Mines / Nowe Kryteria Dla Oceny Zagrożenia Sejsmicznego I Tąpaniami W Kopalniach Węgla Kamiennego

Abstract The paper presents new criteria of seismic and rock burst hazard assessment in Polish hard coal mines where longwall mining system is common practice. The presented criteria are based on the results of continuous recording of seismic events and analysis of selected seismological parameters: spatial location of seismic event in relation to mining workings, seismic energy, seismic energy release per unit coal face advance, b-value of Gutenberg-Richter law, seismic energy index EI, seismic moment M0, weighted value of peak particle velocity PPVW. These parameters are determined in a moving daily time windows or time windows with fixed number of seismic tremors. Time changes of these parameters are then compared with mean value estimated in the analyzed area. This is the basis to indicate the zones of high seismic and rock burst hazard in specific moment in time during mining process. Additionally, the zones of high seismic and rock burst hazard are determined by utilization of passive seismic tomography method. All the calculated seismic parameters in moving time windows are used to quantify seismic and rock burst hazard by four level scales. In practice, assessment of seismic and rock burst hazard is used to make daily decision about using rock burst prevention activities and correction of further exploitation of monitored coal panel.

Does the singular value decomposition entropy have predictive power for stock market? — Evidence from the Shenzhen stock market

This paper analyzes the predictive ability of the singular value decomposition entropy for the Shenzhen Component Index based on different scales. It is found that, the predictive ability of the entropy for the index is affected by the width of moving time windows and the structural break in stock market. By moving time windows with one year, the predictive power of singular value decomposition entropy of Shenzhen stock market for its component index is found after the reform of non-tradable shares.

Detecting changes in coupling with Granger causality method from time series with fast transient processes

The ability of the Granger causality method to detect directed coupling between subsystems of a complex system in a moving time window is investigated on etalon oscillators. In particular, the time series consisting of alternate stationary regimes characterised by the different amplitude and shape of oscillations with fast transient processes between these regimes are considered, with similar transitions being possible due to changes either in the coupling or in the individual properties of subsystems. Two popular approaches to surrogate times series generation are used to check the significance of the method results. Two model structures: the standard linear and the special non-linear adapted to data are implemented.The Granger causality method using the model structure adapted to data is shown to be significantly advantageous in detecting coupling directionality and the instant time of the regime change than the standard linear method, while in some cases the sensitivity and the specificity of the adapted approach are insufficient.