Sequential Time Series Research Articles

Optimal planning and operation of energy storage systems in radial networks for wind power integration with reserve support

Though energy storage system (ESS) is a promising approach to alleviate the variability of non-dispatchable wind power and other forms of renewable energy sources, its high investment cost has impeded its wide deployment. Aiming at exploiting the arbitrage benefit of ESS in reserve market and raising revenue of shareholders, this study explores the optimal planning and operation of ESS in radial networks. Besides load balancing, ESS is used to provide three kinds of operating reserve services in presence of high wind power penetration, including spinning reserve, upward and downward regulation reserves. In light of the capacity limitation of ESS, the time duration of reserve provision has been taken into account. In the proposed model, unit commitment and AC optimal power flow (AC-OPF) are combined together over sequential time series to find the optimal location and size of ESSs. In order to reduce the computational complexity, the extended DistFlow model of AC-OPF is adopted to convert the problem into a mixed-integer second-order cone programming. Numerical studies on the IEEE 34-bus distribution test feeder are used to investigate the effects of ESS with respect to various penetration levels of wind power and load scales.

Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.

Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM10) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM10, which were measured at El Hamma station in Algiers. Artificial neural network models were developed using a combination of meteorological and time-scale as input variables. The results were rather satisfactory, with values of the coefficient of correlation (R (2)) for independent test sets ranging between 0.60 and 0.85 and values of the index of agreement (IA) between 0.87 and 0.96. In addition, the root mean square error (RMSE), the mean absolute error (MAE), the normalized mean squared error (NMSE), the absolute relative percentage error (ARPE), the fractional bias (FB), and the fractional variance (FS) were calculated to assess the performance of the model. It was seen that the overall performance of model 3 was better than models 1 and 2.

Quantitative phase microscopy: automated background leveling techniques and smart temporal phase unwrapping.

In order for time-dynamic quantitative phase microscopy to yield meaningful data to scientists, raw phase measurements must be converted to sequential time series that are consistently phase unwrapped with minimal residual background shape. Beyond the initial phase unwrapping, additional steps must be taken to convert the phase to time-meaningful data sequences. This consists of two major operations both outlined in this paper and shown to operate robustly on biological datasets. An automated background leveling procedure is introduced that consistently removes background shape and minimizes mean background phase value fluctuations. By creating a background phase value that is stable over time, the phase values of features of interest can be examined as a function of time to draw biologically meaningful conclusions. Residual differences between sequential frames of data can be present due to inconsistent phase unwrapping, causing localized regions to have phase values at similar object locations inconsistently changed by large values between frames, not corresponding to physical changes in the sample being observed. This is overcome by introducing a new method, referred to as smart temporal unwrapping that temporally unwraps and filters the phase data such that small motion between frames is accounted for and phase data are unwrapped consistently between frames. The combination of these methods results in the creation of phase data that is stable over time by minimizing errors introduced within the processing of the raw data.

A novel online adaptive time series prediction model with input and output uncertainties

Only the output uncertainty is usually concerned in many regression models for noisy time series prediction, and a near optimal smooth model will be a close approximation to input–output process function if the input data is noise free and only the output data is corrupted by noise in a smooth input–output process. However, if the input data is also corrupted by noise then the best predictive smooth model based on noisy data need not be an approximation to the actual underlying process; rather, the best predictive model depends on both the underlying process and the noise. In this study, a novel strategy with noise addition is proposed by combining iterated nonlinear filters and the single multiplicative neuron (SMN) model, in which the additive noises describe the internal state uncertainty which can be used to explain the input noise and the output uncertainty. The state vector and the observation equation of nonlinear filters are presented by using the weights and the biases of SMN model and the output of SMN model, and the input vector data of the SMN model are composed of known sequential noisy time series data. To verify the effectiveness of the proposed methods, the noisy Mackey–Glass time series, the noisy Box–Jenkins dataset and the noisy electroencephalogram data are employed. The experimental results have demonstrated that the proposed methods are effective for noisy time series prediction.

Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach

This study presents a novel approach of using the hybrid Wavelet Gene Expression Programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data from four different catchments located in the different Hydro-Climatic regions of the world is used in the study. The WGEP model is developed by integrating the discrete wavelet transform (DWT) with the Gene Expression Programming (GEP) models so that individual strengths of each approach can be exploited in a synergistic manner. It is implemented by transforming the input rainfall data using the DWT in order to reveal the temporal and the spectral information contained in the data and subsequently this transformed data is used as input for the GEP models. Ten different mother wavelet functions from different wavelet families are employed in the study in order to transform the data. The mother wavelet functions used in the study include the simple mother wavelet function Haar, the db2, db4, db8 wavelet functions of the most popular wavelet family Daubechies, sym2, sym4, sym8 wavelet functions with sharp peaks, coif2, coif4 wavelet functions and the discrete approximation of meyer (dmey) wavelet. The study further investigates the selection of the optimum input vector to be used in conjunction with the WGEP models by considering nine different input vectors. The first five input vectors contain only one rainfall data series ranging from lagged-1 day to lagged-5 day rainfall data series. The remaining four input vectors are selected using the most common approach in which selection of the input vector comprising of the sequential time series data. The performance of the hybrid WGEP models are compared with the simple GEP models developed without the wavelet transformation for the four selected catchments. The study found that the performance of the WGEP models is superior relative to the simple GEP model for all the nine input vectors considered. However, the WGEP models outperformed their respective simple GEP models only for the first five input vectors. Furthermore, the WGEP models exhibits better results with the dmey wavelet function for all the four catchments during both training and testing.

Energy and heat fluxes due to vertically propagating Yanai waves observed in the equatorial Indian Ocean

AbstractShipboard current measurements in the equatorial Indian Ocean in October and November of 2011 revealed oscillations in the meridional velocity with amplitude . These were clearest in a layer extending from ∼300 to 600 m depth and had periods near 3 weeks. Phase propagation was upward. Measurements from two sequential time series at the equator, four meridional transects and one zonal transect are used to identify the oscillation as a Yanai wave packet and to establish its dominant frequency and vertical wavelength. The Doppler shift is accounted for, so that measured wave properties are translated into the reference frame of the mean zonal flow. We take advantage of the fact that, in the depth range where the wave signal was clearest, the time‐averaged current and buoyancy frequency were nearly uniform with depth, allowing application of the classical theoretical representation of vertically propagating plane waves. Using the theory, we estimate wave properties that are not directly measured, such as the group velocity and the zonal wavelength and phase speed. The theory predicts a vertical energy flux that is comparable to that carried by midlatitude near‐inertial waves. We also quantify the wave‐driven meridional heat flux and the Stokes drift.

Parsimonious rainfall–runoff model construction supported by time series processing and validation of hydrological extremes – Part 1: Step-wise model-structure identification and calibration approach

Summary Common problems faced by rainfall–runoff modellers are data limitation, model overparameterization and related problems of parameter identifiability. Depending on the application, possible solutions to overcome these problems include the use of parsimonious conceptual models, avoid the use of a fixed pre-defined model conceptualization, but apply a “top-down” or “downward” method to allow the model structure to be adjusted or inferred from available data and field evidence. This paper presents a top-down procedure that starts from a generalized model structure framework that is adjusted in a case-specific parsimonious way. The model-structure building is done in a transparent, step-wise way, where separate parts of the model structure are identified and calibrated based on multiple and non-commensurable information derived from river flow series by means of a number of sequential time series processing tasks. These include separation of the high frequency (e.g., hourly, daily) river flow series into subflows, split of the series in approx. independent quick and slow flow hydrograph periods, and the extraction of independent peak and low flows. The model building and calibration account for the statistical assumptions and requirements on independency and homoscedasticity of the model residuals. Next to identification of the subflow recessions and related routing submodels, equations describing quick and slow runoff sub-responses and soil water storage are derived from the time series data. The method includes testing of the model performance for peak and low flow extremes.

On dynamic mode decomposition: Theory and applications

Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken. We present a theoretical framework in which we define DMD as the eigendecomposition of an approximating linear operator. This generalizes DMD to a larger class of datasets, including nonsequential time series. We demonstrate the utility of this approach by presenting novel sampling strategies that increase computational efficiency and mitigate the effects of noise, respectively. We also introduce the concept of linear consistency, which helps explain the potential pitfalls of applying DMD to rank-deficient datasets, illustrating with examples. Such computations are not considered in the existing literature, but can be understood using our more general framework. In addition, we show that our theory strengthens the connections between DMD and Koopman operator theory. It also establishes connections between DMD and other techniques, including the eigensystem realization algorithm (ERA), a system identification method, and linear inverse modeling (LIM), a method from climate science. We show that under certain conditions, DMD is equivalent to LIM.

Rapid-Testing Technology and Systems Improvement for the Elimination of Congenital Syphilis in Haiti: Overcoming the "Technology to Systems Gap".

Background. Despite the availability of rapid diagnostic tests and inexpensive treatment for pregnant women, maternal-child syphilis transmission remains a leading cause of perinatal morbidity and mortality in developing countries. In Haiti, more than 3000 babies are born with congenital syphilis annually. Methods and Findings. From 2007 to 2011, we used a sequential time series, multi-intervention study design in fourteen clinics throughout Haiti to improve syphilis testing and treatment in pregnancy. The two primary interventions were the introduction of a rapid point-of-care syphilis test and systems strengthening based on quality improvement (QI) methods. Syphilis testing increased from 91.5% prediagnostic test to 95.9% after (P < 0.001) and further increased to 96.8% (P < 0.001) after the QI intervention. Despite high rates of testing across all time periods, syphilis treatment lagged behind and only increased from 70.3% to 74.7% after the introduction of rapid tests (P = 0.27), but it improved significantly from 70.2% to 84.3% (P < 0.001) after the systems strengthening QI intervention. Conclusion. Both point-of-care diagnostic testing and health systems-based quality improvement interventions can improve the delivery of specific evidence-based healthcare interventions to prevent congenital syphilis at scale in Haiti. Improved treatment rates for syphilis were seen only after the use of systems-based quality improvement approaches.

Monitoring Influenza Epidemics in China with Search Query from Baidu

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China.

Reuse and Recycle: The Development of Adaptive Expertise, Routine Expertise, and Novelty in a Large Research Team

SummaryCombining innovation and efficiency is ideal in many organizational settings. Adaptive expertise represents a cognitive explanation of how individuals and teams can learn to achieve simultaneous innovation and efficiency. In 2004, scientists led twin rovers on Mars in the search for historical water. The science team experienced a remarkable increase in efficiency, adapting with flexibility to unexpected events and dynamic, dwindling resources. After discussing the conceptual differences between adaptive expertise and related team learning and innovation concepts, we examine longitudinal behavioral data on novelty, routine and adaptive expertise. Sequential time series ARIMA analyses reveal that novelty fluctuated randomly, but both routine and adaptive expertise significantly increased over time. In addition, novelty, routine expertise, and adaptive expertise did not significantly predict each other directly or at a lag, suggesting that these are indeed three distinct constructs. Implications for theory and research on efficiency and innovation are discussed. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Unsupervised Land Cover Change Detection: Meaningful Sequential Time Series Analysis

An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short term Fourier transform coefficients computed over subsequences of 8-day composite MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance data that were extracted with a temporal sliding window. The method uses a feature extraction process that creates meaningful sequential time series that can be analyzed and processed for change detection. The method was evaluated on real and simulated land cover change examples and obtained a change detection accuracy exceeding 76% on real land cover conversion and more than 70% on simulated land cover conversion.

Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling

Summary This study investigates the effect of inputs used on event-based runoff forecasting by ANFIS. Fifteen ANFIS models were compared, differentiated by the choice of rainfall and/or discharge inputs used. Inputs to the ANFIS models consisted of (i) two models consisting of n + 1 inputs of a sequential rainfall time series ( R t , R t −1 , R t −2 , … , R t − n ), (ii) four models with a pruned sequential rainfall time series where a narrower time window of sequential rainfall inputs were used, and (iii) one model with two non-sequential inputs of rainfall inputs, R t - τ 1 and R t - τ 2 , where R t - τ 1 and R t - τ 2 are the antecedent rainfall at time t - τ 1 and t - τ 2 , respectively. The rest of the models include ANFIS models which have the same inputs as those mentioned except that the antecedent discharge, Q ( t − 1), was included, and the last model which used only Q ( t − 1) as input. The models were evaluated by considering the goodness-of-fit, peak discharge estimation and time shift. It was found that models using only rainfall antecedents as inputs performed better in term of goodness-of-fit for discharge at larger lead times (up to eight time steps ahead) while models which included Q ( t − 1) as input were better in forecasts at shorter lead times (up to two time steps ahead). Models using rainfall only showed smaller time shift error compared to those that contain Q ( t − 1). The overall comparison shows that the model that used a non-sequential rainfall time series and an antecedent discharge performed well for forecasts up to two time steps ahead while the model that used a non-sequential rainfall time series gave superior results for forecasts greater than two time steps ahead. Models using the pruned sequential rainfall antecedents were found to be unreliable and models using sequential rainfall antecedents were found to give the worst results.

Maximizing Firm Wind Connection to Security Constrained Transmission Networks

Prudent use of existing transmission capacity could be achieved by an optimal allocation of wind capacity to distinct transmission nodes. The statistical interdependency of geographically separate wind sites and the partially-dispatchable nature of wind power require a collective analysis of all potential wind farms over an extended time-frame in any optimized transmission planning study. The methodology presented in this paper separates this large optimization problem into smaller subtasks, including a year-long sequential time series hourly integer unit commitment, a linear dc load-flow network model with hourly security constraints, and a linear programming optimization model to estimate the maximum firm wind energy penetration for a given network. A novel maximal- vector based constraint redundancy analysis is employed to significantly reduce the linear programming optimization dimensionality. Firm wind capacity connections are facilitated in this paper - i.e., those to which wind curtailment to manage congestion is not applicable within a typical system ?planning? timeframe analysis. Each bus is allocated firm capacity on the basis of maximizing the possible firm wind energy penetration in the transmission system as a whole, while preserving traditional network security standards.

A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models

A multi-criteria model evaluation protocol is presented to check the performance of rainfall-runoff models during model calibration and validation phases based on a high frequency (e.g. hourly, daily) river flow series. The multiple criteria or objectives are based on multiple and non-commensurable measures of information derived from river flow series by means of a number of sequential time series processing tasks. These include separation of the river flow series in subflows, split of the series in nearly independent quick and slow flow hydrograph periods, and the extraction of nearly independent peak and low flows. The protocol accounts for the statistical assumptions and requirements on independency and homoscedasticity of the model residuals, significantly advanced through the use of nearly independent flow values extracted from the flow series. Next to the separate evaluation of the subflow recessions, the quick and slow runoff peak and low values and event volumes, also the performance of the model in predicting extreme high and low flow statistics is validated. To support the time series processing tasks as well as the application of the multi-criteria model evaluation protocol, a Microsoft Excel-based tool (WETSPRO: Water Engineering Time Series PROcessing tool) has been developed. It is based on the assessment of graphical displays, which complement traditional goodness-of-fit statistics.

A Temporal, Multicity Model to Estimate the Effects of Short-Term Exposure to Ambient Air Pollution on Health

BackgroundCountries worldwide are expending significant resources to improve air quality partly to improve the health of their citizens. Are these societal expenditures improving public health?ObjectivesWe consider these issues by tracking the risk of death associated with outdoor air pollution over both space and time in Canadian cities.Materials and methodsWe propose two multi-year estimators that use current plus several previous years of data to estimate current year risk. The estimators are derived from sequential time series analyses using moving time windows. To evaluate the statistical properties of the proposed methods, a simulation study with three scenarios of changing risk was conducted based on 12 Canadian cities from 1981 to 2000. Then an optimal estimator was applied to 24 of Canada’s largest cities over the 17-year period from 1984 to 2000.ResultsThe annual average daily concentrations of ozone appeared to be increasing over the time period, whereas those of nitrogen dioxide were decreasing. However, the proposed method returns different time trends in public health risks. Evidence for some monotonic increasing trends in the annual risks is weak for O3 (p = 0.3870) but somewhat stronger for NO2 (p = 0.1082). In particular, an increasing time trend becomes apparent when excluding year 1998, which reveals lower risk than proximal years, even though concentrations of NO2 were decreasing. The simulation results validate our two proposed methods, producing estimates close to the preassigned values.ConclusionsDespite decreasing ambient concentrations, public health risks related to NO2 appear to be increasing. Further investigations are necessary to understand why the concentrations and adverse effects of NO2 show opposite time trends.

Evaluation of a modelling system for predicting the concentrations of PM 2.5 in an urban area

We present a modelling system that contains a treatment of the emissions and atmospheric dispersion of fine particulate matter (PM 2.5) on an urban scale, combined with a statistical model for estimating the contribution of long-range transported aerosols. The model of PM 2.5 emissions includes exhaust emissions, cold starts and driving, as well as, non-exhaust emissions originated from urban vehicular traffic. The influence of primary vehicular emissions from the road and street network was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. We have computed hourly sequential time series of the PM 2.5 concentrations in 2002 in a numerical grid in the Helsinki Metropolitan Area. The predicted results were compared against measured data at two locations in central Helsinki: urban roadside station of Vallila and urban background station of Kallio. The predicted daily average PM 2.5 concentrations agreed well with the measured values; e.g., the index of agreement values were 0.83 and 0.86 at Vallila and Kallio, respectively, and the absolute values of fractional bias ⩽0.13. As expected, the scatter of data points is substantially wider for the hourly concentration values; e.g., the index of agreement values were 0.69 and 0.74. We also computed the spatial concentration distributions of PM 2.5. The predicted contribution from long-range transport to the street level PM 2.5 varied spatially from 40% in the most trafficked areas to nearly 100% in the outskirts of the area. The emissions originated from cold starts and driving were responsible for <4% of the annual average concentrations at the roadside and urban stations. The model can potentially be used as a practical tool of assessment of urban PM 2.5 contributions in various European regions.

Effects of a Color Filter Used in Auriculomedicine on Ultraweak Photon Emission of the Human Body

The human body emits ultraweak photons. It has been demonstrated that feedback regulation of ultraweak photon emission from the hand is detectable utilizing gelatin color filters in complete darkness. Color filters are commonly utilized in auriculomedicine diagnostic procedures based on the radial artery vascular autonomic signal. To evaluate the possibility of an effect by a dark-adapted red filter on (1) local ultraweak human photon emission using different anatomic locations, and (2) on systemic photon emission using one location for red filter exposure and another for photon emission recording. Photon emission of abdomen, forehead, palm, and dorsum of the hand was recorded before and after the presence of a red filter. In addition, photon emission of forehead and dorsum of the hand was recorded before, during, and after the presence of the red filter close to the palm. In both instances, the filter was dark-adapted and placed in close proximity to, but not touching the skin of the dark-adapted subject. A low-noise, end window photomultiplier for recording ultraviolet and visible (200-650 nm) photon emission (with a very low background count rate) and designed for manipulation in three directions was situated in a dark room. The technology was utilized to record spontaneous photon emission of the abdomen, forehead, palm, and dorsum of the hand of one human male subject. Data demonstrate that a dark-adapted subject responds in the total absence of light with a temporary increase in photon emission from the anatomic locations that had been exposed for 200 s at a distance of 3 cm to a dark-adapted red filter. Data from sequential time series suggest red filter initiation of a refractory emission reaction. Exposure of the palm to the red filter also evokes photon emission from the dorsum of the same hand and from the forehead. This response could be registered immediately after the beginning of the exposure. The stimulated emission is followed by a slow decrease of emission in the period after exposure to the filter. Data suggest that red filter-stimulated photon emission response is systemic and rapid. Data imply an exchange of information vis-à-vis light from both hand and red filter. The mechanism of such interaction is currently speculation only.

Making clustering in delay-vector space meaningful

Sequential time series clustering is a technique used to extract important features from time series data. The method can be shown to be the process of clustering in the delay-vector space formalism used in the Dynamical Systems literature. Recently, the startling claim was made that sequential time series clustering is meaningless. This has important consequences for a significant amount of work in the literature, since such a claim invalidates these work’s contribution. In this paper, we show that sequential time series clustering is not meaningless, and that the problem highlighted in these works stem from their use of the Euclidean distance metric as the distance measure in the delay-vector space. As a solution, we consider quite a general class of time series, and propose a regime based on two types of similarity that can exist between delay vectors, giving rise naturally to an alternative distance measure to Euclidean distance in the delay-vector space. We show that, using this alternative distance measure, sequential time series clustering can indeed be meaningful. We repeat a key experiment in the work on which the “meaningless” claim was based, and show that our method leads to a successful clustering outcome.

Evaluation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations

In this paper, a multi-layer perceptron (MLP) model and the Finnish variant of the numerical weather prediction model HIRLAM (High Resolution Limited Area Model) were integrated and evaluated for the forecasting in time of urban pollutant concentrations. The forecasts of the combination of the MLP and HIRLAM models are compared with the corresponding forecasts of the MLP models that utilise meteorologically pre-processed input data. A novel input selection method based on the use of a multi-objective genetic algorithm (MOGA) is applied in conjunction with the sensitivity analysis to reduce the excessively large number of potential meteorological input variables; its use improves the performance of the MLP model. The computed air quality forecasts contain the sequential hourly time series of the concentrations of nitrogen dioxide (NO 2) and fine particulate matter (PM 2.5) from May 2000 to April 2003; the corresponding concentrations have also been measured at two urban air quality stations in Helsinki. The results obtained with the MLP models that use HIRLAM forecasts show fairly good overall agreement for both pollutants. The model performance is substantially better, when the HIRLAM forecasts are used, compared with those obtained both using either HIRLAM analysis data or meteorological pre-processor, for both pollutants. The performance of the currently widely used statistical forecasting methods (such as those based on neural networks) could therefore be significantly improved by using the forecasts of NWP models, instead of the conventionally utilised directly measured or meteorological pre-processed input data. However, the performance of all operational models considered is relatively worse in the course of air pollution episodes.