True Time Series Research Articles

Development of a continuous UAV-mounted air sampler and application to the quantification of CO2 and CH4 emissions from a major coking plant

Abstract. The development in uncrewed aerial vehicle (UAV) technologies over the past decade has led to a plethora of platforms that can potentially enable greenhouse gas emission quantification. Here, we report the development of a new air sampler, consisting of a pumped stainless coiled tube of 150 m in length with controlled time stamping, and its deployment from an industrial UAV to quantify CO2 and CH4 emissions from the main coking plant stacks of a major steel maker in eastern China. Laboratory tests show that the time series of CO2 and CH4 measured using the sampling system is smoothed when compared to online measurement by the cavity ring-down spectrometer (CRDS) analyzer. Further analyses show that the smoothing is akin to a convolution of the true time series signals with a heavy-tailed digital filter. For field testing, the air sampler was mounted on the UAV and flown in virtual boxes around two stacks in the coking plant of the Shagang Group (steel producer). Mixing ratios of CO2 and CH4 in air and meteorological parameters were measured from the UAV during the test flight. A mass-balance computational algorithm was used on the data to estimate the CO2 and CH4 emission rates from the stacks. Using this algorithm, the emission rates for the two stacks from the coking plant were calculated to be 0.12±0.014 t h−1 for CH4 and 110±18 t h−1 for CO2, the latter being in excellent agreement with material-balance-based estimates. A Gaussian plume inversion approach was also used to derive the emission rates, and the results were compared with those derived using the mass-balance algorithm, showing a good agreement between the two methods.

Efficacy of cling film dressing on palmoplantar psoriatic lesions among patients with psoriasis: A randomized controlled trial

Introduction: Caring for patients with skin disorders is “more than skin deep”. Psoriasis is a systematic immune-mediated disease that generally does not affect survival, but certainly has major negative effects on patients. The previous research has reported that artificial restoration of a permeable barrier by occlusion results in regression of lesions in psoriasis.The aim of this research is to assess the efficacy of cling film dressing versus conventional treatment on palmoplantar psoriatic lesions among patients with psoriasis. Methods: The research design used in the study was a true experimental time series design. Block randomization was used to assign the study participants into either study or control group in a Psoriatic Out-Patient Department of tertiary care center at Southern part of India. As usual, the intervention group participants received the application of topical corticosteroid Eczivate MF with cling film wrap while the control group participants followed the conventional treatment (topical corticosteroid Eczivate MF without occlusive dressing). The modified psoriasis severity index (MPSI) score was used to measure the outcomes. Results: There was a high statistical significance difference on the severity of psoriatic disease level between the control and study groups (p ˂ 0.05). Conclusion: Steroidal cream application with occlusive dressing yielded better results than non-occlusive open treatment.

Power-laws in dog behavior may pave the way to predictive models: A pattern analysis study

Apparently random events in nature often reveal hidden patterns when analyzed using diverse and robust statistical tools. Power law distributions, for example, project diverse natural phenomenon, ranging from earthquakes to heartbeat dynamics into a common platform of self-similarity. Animal behavior in specific contexts has been shown to follow power law distributions. However, the behavioral repertoire of a species in its entirety has never been analyzed for the existence of such underlying patterns. Here we show that the frequency-rank data of randomly sighted behaviors at the population level of free-ranging dogs follow a scale-invariant power law behavior. It suggests that irrespective of changes in location of sightings, seasonal variations and observer bias, datasets exhibit a conserved trend of scale invariance. The data also exhibits robust self-similarity patterns at different scales which we extract using multifractal detrended fluctuation analysis. We observe that the probability of consecutive occurrence of behaviors of adjacent ranks is much higher than behaviors widely separated in rank. The findings open up the possibility of designing predictive models of behavior from correlations existing in true time series of behavioral data and exploring the general behavioral repertoire of a species for the presence of syntax.

Regulatory Dynamics of Cell Differentiation Revealed by True Time Series From Multinucleate Single Cells.

Dynamics of cell fate decisions are commonly investigated by inferring temporal sequences of gene expression states by assembling snapshots of individual cells where each cell is measured once. Ordering cells according to minimal differences in expression patterns and assuming that differentiation occurs by a sequence of irreversible steps, yields unidirectional, eventually branching Markov chains with a single source node. In an alternative approach, we used multi-nucleate cells to follow gene expression taking true time series. Assembling state machines, each made from single-cell trajectories, gives a network of highly structured Markov chains of states with different source and sink nodes including cycles, revealing essential information on the dynamics of regulatory events. We argue that the obtained networks depict aspects of the Waddington landscape of cell differentiation and characterize them as reachability graphs that provide the basis for the reconstruction of the underlying gene regulatory network.

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals.

Satellite soil moisture and vegetation optical depth [(VOD); related to the total vegetation water mass per unit area] are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on subweekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

A study to Evaluate the Effectiveness of Oral Stimulation and Oral Support on Feeding performances among preterm and low birth weight babies (LBW) at R.L Jalappa Hospital &Research Centre, Kolar, Karnataka.

Introduction Preterm infants frequently experience oral feeding difficulties because of underdeveloped central nervous system and oral musculature with lack of coordination for the suck-swallow-breath mechanism which make them rely on artificial feedings and parenteral nutrition to meet their nutritional requirements. Objective To assess and compare the effectiveness of Oral Stimulation and Oral support on feeding performances among Preterm and Low Birth Weight babies. Methods and Material Quantitative approach with True Experimental Time Series design was used for the study with sample size of 40 by using simple random sampling technique. Results Mean scores with SD of feeding performance of Experimental group on Day-3 5 amp7 was 19.85plusmn5.00 26.80plusmn3.41 amp 28.35plusmn1.72 significantly higher than the control group 13.70plusmn6.08 19.30plusmn6.68 amp 23.85plusmn6.02 with lsquotrsquo values 3.48 4.46 and 3.21 respectively plt0.001 indicating statistically significant difference in feeding performances between experimental and control group at 0.05 level of significance Conclusions Study concludes that Oral stimulation and Oral support helps in improving the feeding performances of Preterm and LBW babies as early establishment of breast feeding is the most common problem among the babies born premature and LBW.nbsp

Simple IoT simulation

While analyzing iot projects it is very expensive to buy a lot of sensors , corresponding processor boards, power supplies etc. Moreover the entire process is to be replicated to cater to large topologies. The whole experiment is to be planned at a large scale before we can actually start to see analytics working. At a smaller scale this can be implemented as a simulation program in linux where the sensor data is created using a random number generator and scaled appropriately for each type of sensor to mimic representative data. This is them encrypted before sending it over the network to the edge nodes. At the server a socket stream now continuously awaits sensor data Here the required sensor data is retrieved and decrypted to give true time series data. This time series is now given to an analytics engine which calculates the trends and cyclicity and is used to train a neural network. The anomalies so found are properly deciphered. The multiplicity of the nodes can be characterized by having several client programs running in separate terminals. A simple client server architecture is thus able to simulate a large iot infrastructure and is able to perform analytics on a scaled model

Carabid beetles in a regional and a country-wide elevation gradient of Bavarian forests: differences and thoughts on the value of false time series approaches

We studied the effects of global warming and rising temperatures on the ground beetle fauna of Bavarian beech forests using the space for time approach (false time series), and did so in two projects, at two geographical scales: firstly, a Bavarian-wide gradient of 50 plots in beech forests, and second a regional gradient in the Bavarian Forest in the mountains in eastern Bavaria consisting of 48 plots, which was not restricted to beech forests but also included subalpine spruce forests. For purposes of validation, we used as a backdrop data from 413 additional plots all across Bavaria from a wide range of forest habitats. We found five species that would be favoured and six species that would be disadvantaged by rising temperatures in beech forests. For another five species the conditions within the gradient studied reach both their minimum and their maximum temperatures. As a consequence of increasing temperatures there will be winners and losers in these forests and the species composition of ground beetle communities will change. Approximately the same number of species is likely to profit as will be affected negatively. However, when considering the “global responsibility species” for Germany, the balance is negative. It is imporant to note that species may react differently in different habitats and at different regional scales. Some species migtht be more endangered in a local gradient than in a regional gradient, and vice versa, and examples are provided for this. Climate effects proven for a certain habitat must not be the same for other habitats even within the same region if these were not included in the study design. These limitations must be taken into consideration when applying the results of such studies and especially when generalizing them. The benefits of a true time series compared to the popular "false time serices" provided by an elevational gradient are also highlighted using some examples.

Nitrogen leaching from natural ecosystems under global change: a modelling study

Abstract. To study global nitrogen (N) leaching from natural ecosystems under changing N deposition, climate, and atmospheric CO2, we performed a factorial model experiment for the period 1901–2006 with the N-enabled global terrestrial ecosystem model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator). In eight global simulations, we used either the true transient time series of N deposition, climate, and atmospheric CO2 as input or kept combinations of these drivers constant at initial values. The results show that N deposition is globally the strongest driver of simulated N leaching, individually causing an increase of 88 % by 1997–2006 relative to pre-industrial conditions. Climate change led globally to a 31 % increase in N leaching, but the size and direction of change varied among global regions: leaching generally increased in regions with high soil organic carbon storage and high initial N status, and decreased in regions with a positive trend in vegetation productivity or decreasing precipitation. Rising atmospheric CO2 generally caused decreased N leaching (33 % globally), with strongest effects in regions with high productivity and N availability. All drivers combined resulted in a rise of N leaching by 73 % with strongest increases in Europe, eastern North America and South-East Asia, where N deposition rates are highest. Decreases in N leaching were predicted for the Amazon and northern India. We further found that N loss by fire regionally is a large term in the N budget, associated with lower N leaching, particularly in semi-arid biomes. Predicted global N leaching from natural lands rose from 13.6 Tg N yr−1 in 1901–1911 to 18.5 Tg N yr−1 in 1997–2006, accounting for reductions of natural land cover. Ecosystem N status (quantified as the reduction of vegetation productivity due to N limitation) shows a similar positive temporal trend but large spatial variability. Interestingly, this variability is more strongly related to vegetation type than N input. Similarly, the relationship between N status and (relative) N leaching is highly variable due to confounding factors such as soil water fluxes, fire occurrence, and growing season length. Nevertheless, our results suggest that regions with very high N deposition rates are approaching a state of N saturation.

A dynamic exchange rate model with heterogeneous agents

In this paper, we analyze a heterogeneous agent model in which the fundamental exchange rate is endogenously determined by the real markets. The exchange rate market and the real markets are linked through the balance of payments. We have analytically found that there exists at least a steady state in which the exchange rate is equal to its fundamental value and incomes of both countries are equal to the autonomous components times the multiplier (as in the Income-Expenditure model). This steady state can be unique and unstable when all agents act as contrarians, while when agents act as fundamentalists it is unique but its stability depends on the reactivity of actors of the market. Finally, we show that the (in)stability of the economic system depends on both the reactivity of the markets and that of different types of agents involved. Employing well-know functional forms, we show that the model can replicate some of the statistical features of the true time series of the exchange rate.

Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

A symbolic encoding scheme, based on the ordinal relation between the amplitude of neighboring values of a given data sequence, should be implemented before estimating the permutation entropy. Consequently, equalities in the analyzed signal, i.e. repeated equal values, deserve special attention and treatment. In this work, we carefully study the effect that the presence of equalities has on permutation entropy estimated values when these ties are symbolized, as it is commonly done, according to their order of appearance. On the one hand, the analysis of computer-generated time series is initially developed to understand the incidence of repeated values on permutation entropy estimations in controlled scenarios. The presence of temporal correlations is erroneously concluded when true pseudorandom time series with low amplitude resolutions are considered. On the other hand, the analysis of real-world data is included to illustrate how the presence of a significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts.

High Dimensional Nonstationary Time Series Modelling with Generalized Dynamic Semiparametric Factor Model

(High dimensional) time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science. In this article, we separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via functional factor analysis. We propose a two-step estimation procedure. At the first step, we detect the deterministic trends of the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under various situations extending current variable selection studies. At the second step, we obtain the detrended low dimensional stochastic process, but it also poses an important question: is it justified, from an inferential point of view, to base further statistical inference on the estimated stochastic time series? We show that the difference of the inference based on the estimated time series and true unobserved time series is asymptotically negligible, which finally allows one to study the dynamics of the whole high-dimensional system with a low dimensional representation together with the deterministic trend. We apply the method to our motivating empirical problems: studies of the dynamic behavior of temperatures (further used for pricing weather derivatives), implied volatilities and risk patterns and correlated brain activities (neuro-economics related) using fMRI data, where a panel version model is also presented.

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.

Minimizing the effects of filtering on catchment scale GRACE solutions

AbstractThe Gravity Recovery and Climate Experiment (grace) satellite mission has provided time variable gravity information since its launch in 2002. Due to short‐wavelength noise, the total water storage variations over a catchment observed from grace are usable only after filtering. Filtering smooths both the signal and the noise, inevitably changing the nature of the estimated total water storage change. The filtered estimates suffer from attenuation and leakage, which changes the signal characteristics. Several studies have mainly focused on correcting the changed amplitude with the aid of hydrological models. In this study, it is demonstrated that in addition to the amplitude loss, also significant phase change in the time series of total water storage over a region can occur. The phase change due to leakage from nearby catchments can be around to for catchments with moderate size, which makes it difficult to retrieve signal by only scaling. We propose a strategy to approach the true time series with improved phase and amplitude. The strategy is independent of any hydrological model. It is first demonstrated in a closed‐loop environment over 32 catchments, where we show that the performance of our method is consistent and better than other model‐dependent approaches. Then we also discuss the limitations of our approach. Finally we apply our method to the grace level 2 products for 32 catchments.

How Much Information Does Dependence Between Wavelet Coefficients Contain?

ABSTRACTThis article is motivated by several articles that propose statistical inference where the independence of wavelet coefficients for both short- as well as long-range dependent time series is assumed. We focus on the sample variance and investigate the influence of the dependence between wavelet coefficients and this statistic. To this end, we derive asymptotic distributional properties of the sample variance for a time series that is synthesized, ignoring some or all dependence between wavelet coefficients. We show that the second-order properties differ from the those of the true time series whose wavelet coefficients have the same marginal distribution except in the independent Gaussian case. This holds true even if the dependency is correct within each level and only the dependence between levels is ignored. In the case of sample autocovariances and sample autocorrelations at lag one, we indicate that first-order properties are erroneous. In a second step, we investigate several nonparametric bootstrap schemes in the wavelet domain, which take more and more dependence into account until finally the full dependency is mimicked. We obtain very similar results, where only a bootstrap mimicking the full covariance structure correctly can be valid asymptotically. A simulation study supports our theoretical findings for the wavelet domain bootstraps. For long-range-dependent time series with long-memory parameter d > 1/4, we show that some additional problems occur, which cannot be solved easily without using additional information for the bootstrap. Supplementary materials for this article are available online.

A Bayesian model for microsaccade detection

Microsaccades (or small saccades) are fixational eye movements with high velocity. They have been proposed as an index for covert spatial attention, but this proposal has been contested. One reason why it has been difficult to reach consensus is that different studies use different, arbitrary detection criteria such as velocity thresholds. Here, we developed a principled method for identifying microsaccades, based on Bayesian changepoint detection. Our generative model contains a latent state variable that changes between drift/tremor and microsaccade states at random times. We model the eye position as a biased random walk with a different velocity distribution for each state; on average, microsaccades have higher speed. Using this generative model, we computed the posterior probability over the time series of the state variable given the entire eye position time series. To sample from this high-dimensional posterior while avoiding local maxima, we used parallel-tempered MCMC. To test the validity of our algorithm, we applied it to simulated eye position data from the generative model. At low noise levels, we recovered the true microsaccades near perfectly, while at higher noise levels, we found state vectors with higher posterior probabilities than the true time series. When we apply the algorithm to real data, the inferred microsaccades are comparable with those found by previous methods. Our approach has advantages over previous methods: (1) the detection criterion is derived, not assumed, (2) we obtain a probabilistic judgment (i.e. a confidence rating), instead of a binary one, (3) the method can be straightforwardly adapted as the generative model is refined. Meeting abstract presented at VSS 2015.

A Bayesian Optimization Approach to Multimodel Ensemble Kalman Filter with a Low-Order Model

Abstract Multimodel ensemble data assimilation may account for uncertainties of numerical models due to different dynamical cores and physics parameterizations. In the previous studies, the ensemble sizes for each model are prescribed subjectively, for example, uniformly distributed to each model. In this study, a Bayesian filter approach to a multimodel ensemble Kalman filter is adopted to objectively estimate the optimal combination of ensemble sizes for each model. An effective inflation method to make the discrete Bayesian filter work without converging to a single imperfect model was developed. As a first step, the proposed approach was tested with the 40-variable Lorenz-96 model. Different values of the model parameter F are used to mimic the multimodel ensemble. The true F is first chosen to be , and the observations are generated by adding independent Gaussian noise to the true time series. When the multimodel ensemble consists of , 7, 8, 9, and 10, the Bayesian filter finds the true model and converges to quickly. When , 7, 9, and 10, the closest two models, and F = 9, are selected. When the true F has a periodic variation about with a time scale much longer than the observation frequency, the proposed system follows the temporal change, and the error becomes less than that of the time-invariant optimal combination. Sensitivities to several parameters in the proposed system were also investigated, and the system was found to show improvements in a wide range of parameters.

Assessing the convolutedness of multivariate physiological time series.

A feature of time-series variability that may reveal underlying complex dynamics is the degree of "convolutedness". For multivariate series of m components, convolutedness can be defined as the propensity of the trail of the time-series samples to fill the m-dimensional space. This work proposes different convolutedness indices and compare them on synthesized and real physiological signals. The indices are based on length L and planar extension d of the trail in m dimensions. The classical ones are: the L/d ratio, and the Mandelbrot's fractal dimension (FD) of a curve: FDM =log(L)/log(d). In this work we also consider a correction of the Katz's estimator of FDM, i.e., FDKC =log(N)/(log(N)+log(d/L)), with N the number of samples; and FDMC, an estimator of FDM based on FDKC calculated over a shorter running window Nw<;N appropriately selected to reduce estimation bias. Synthesized fractional Brownian motions indicated that all the indices increase with FD, but differ for other aspects, namely the dependence on N; the capacity to estimate FD; or to distinguish between true bivariate and degenerate bivariate time series. Application on real multivariate recordings of muscular activity before and after exercise-induced fatigue suggests that these indices can be profitably used to identify complex changes in the dynamics of physiological signals.

CONSTRAINING TYPE Ia SUPERNOVAE PROGENITORS FROM THREE YEARS OF SUPERNOVA LEGACY SURVEY DATA

While it is generally accepted that Type Ia supernovae are the result of the explosion of a carbon-oxygen White Dwarf accreting mass in a binary system, the details of their genesis still elude us, and the nature of the binary companion is uncertain. Kasen (2010) points out that the presence of a non-degenerate companion in the progenitor system could leave an observable trace: a flux excess in the early rise portion of the lightcurve caused by the ejecta impact with the companion itself. This excess would be observable only under favorable viewing angles, and its intensity depends on the nature of the companion. We searched for the signature of a non-degenerate companion in three years of Supernova Legacy Survey data by generating synthetic lightcurves accounting for the effects of shocking and comparing true and synthetic time series with Kolmogorov-Smirnov tests. Our most constraining result comes from noting that the shocking effect is more prominent in rest-frame B than V band: we rule out a contribution from white dwarf-red giant binary systems to Type Ia supernova explosions greater than 10% at 2 sigma, and than 20% at 3 sigma level.