Non-stationarity Of Data Research Articles

Sensitivity of the Empirical Mode Decomposition to Interpolation Methodology and Data Non-stationarity

Empirical mode decomposition (EMD) is a commonly used method in environmental science to study environmental variability in specific time period. Empirical mode decomposition is a sifting process that aims to decompose non-stationary and non-linear data into their embedded modes based on the local extrema. The local extrema are connected by interpolation. The results of EMD strongly impact the environmental assessment and decision making. In this paper, the sensitivity of EMD to different interpolation methods, linear, cubic, and smoothing-spline, is examined. A range of non-stationary data, including linear, quadratic, Gaussian, and logarithmic trends as well as noise, is used to investigate the method’s sensitivity to different types of non-stationarity. The EMD method is found to be sensitive to the type of non-stationarity of the input data, and to the interpolation method in recovering low-frequency signals. Smoothing-spline interpolation gave overall the best. The accuracy of the method is also limited by the type of non-stationarity: if the data have an abrupt change in amplitude or a large change in the variance, the EMD method cannot sift correctly.

Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network

The network traffic prediction is significant for the network load pre-warning and network congestion control. But the nonlinearity and nonstationarity of the actual network traffic data would reduce the prediction accuracy. In this paper, an optimized network traffic prediction method using variational mode decomposition (VMD) and multi-reservoirs echo state network (ESN) is presented. VMD method has advantages of reducing the signal transmission errors, removing the mode aliasing, and decreasing the degree of endpoint effects. However, VMD needs to preset the number of modes and the iterative factor, which are mainly decided by subjective experiences. In order to solve this, an optimized VMD method is proposed, and then a multi-reservoirs echo state network based prediction model is constructed. The main works are as follows: First, VMD is used to decompose the original network traffic data into several subsets; then, multiple subreservoirs are built after the phase space reconstruction (PSR) of each data subset; finally, the training set is used to train the prediction model. Moreover, in the training process, an improved fruit fly optimization algorithm (IFOA) is proposed combined with the levy’s flight function and the cloud generator, which is used to optimize some model parameters. Compared with several prediction models, the proposed VMD-IFOA-ESN has better predictive stability and convergence performance. Three WIDE backbone network traffic data sets with different time intervals verify the effectiveness of the proposed prediction model.

景观生态视角下草地生态质量的空间差异及其影响因素——以内蒙古四子王旗为例

PDF HTML阅读 XML下载 导出引用 引用提醒 景观生态视角下草地生态质量的空间差异及其影响因素——以内蒙古四子王旗为例 DOI: 10.5846/stxb201809091928 作者: 作者单位: 内蒙古农业大学,内蒙古农业大学,内蒙古大学,内蒙古农业大学,内蒙古农业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31861143001，31660108） Analysis of spatial differentiation of grassland ecological quality and its affecting factors based on landscape ecology perspective: A case study of Siziwang Banner Author: Affiliation: Inner Mongolia Agriculture University,Inner Mongolia Agriculture University,Inner Mongolia University,Inner Mongolia Agriculture University,Inner Mongolia Agriculture University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:科学评价北方荒漠草原的生态质量，有助于草地景观的生态修复与荒漠化防治。以内蒙古四子王旗为例，采用景观生态视角，通过构建生态质量评价体系，结合综合指数法和自然间断法，对研究区内130个行政村域的草地生态质量进行综合评价及类别划定；通过引入地理加权回归模型并对比最小二乘回归模型，评估外源压力对草地生态质量的影响。结果表明：（1）2016年四子王旗村域草地生态质量的指数变化幅度为0.003-0.765，呈现出北高南低的区域差异；在空间分布上，高高集聚、低低集聚和低高集聚的空间特征反映出村域草地生态质量的非均衡特征及空间依赖；（2）对比模型估计参数，地理加权回归模型具有解释草地生态质量空间异质性和处理数据非平稳性的优势，预测精度优于最小二乘回归模型；（3）6种变量对草地生态质量的回归系数表明，经济压力和环境压力的影响效应明显高于人口压力和资源压力。与草地生态质量变化呈正相关的因素依次为：生态用地比、人均耕地面积和草地载畜量，而与至城镇中心距离、劳动人口比和人均GDP则依次呈现出负相关关系。研究结果可为草地生态保护与治理提供参考。 Abstract:Scientific evaluation of ecological quality of desert steppe in North China is helpful for ecological restoration and desertification control.Taking Siziwang Banner as an example, this paper adopts the landscape ecological perspective, constructs the ecological quality evaluation system, and combines the comprehensive index method to research grassland ecological quality. In this paper, grassland ecological quality of desert steppe in 130 administrative villages is comprehensively evaluated and classified with the natural breaking method. After comparing the ordinary least square regression model, the effect of exogenous pressure on grassland ecological quality was evaluated by introducing the geographical weighted regression model. The main results were as follows:(1) The grassland ecological quality indexes ranged from 0.003 to 0.765 in the village area of Siziwang Banner, showing the regional difference in north high to south low in 2016. The spatial characteristics of High-High agglomeration, Low-Low agglomeration and Low-High agglomeration reflect the disequilibrium characteristics and spatial dependence.(2) The GWR model has the advantage of explaining spatial heterogeneity and processing data nonstationarity of grassland ecological quality. Compared with the estimated parameters, the prediction accuracy of the GWR model is higher than the OLS model. (3) The regression coefficient of six variables on grassland ecological quality showed that the effect of economic pressure and environmental pressure was obviously higher than that of population pressure and resource pressure.(4)The positive correlation factors of the change of grassland ecological quality are as follows:ecological land ratio, per capital amount of farmland and grassland grazing capacity. The distance from the nearest town, labor to force ratio and per capital GDP showed a negative correlation in turn. The results can be used as reference to grassland ecological protection and management. 参考文献 相似文献 引证文献

Modelling spatial and temporal dynamics of two small mud carp species in the Tonle Sap flood-pulse ecosystem

The main challenges for ecological studies are the complexity and non-stationarity of data that are difficult to handle using traditional modelling methods. Thus, to address these problems, modern modelling techniques have been developed and introduced for applications. In time-series data, the cross-wavelet transform (CWT) and cross-correlation function (CCF) serve as promising tools to analyze the dynamics of fish populations through time and space. Here, we employed these two well-known time-series modelling approaches to model the spatial and temporal dynamics of small mud carp species (Henicorhynchus lobatus and Henicorhynchus siamensis) and examine the influence of flow pulses on their dynamics using spatial and time-series data collected from the Tonle Sap Lake and River System. The phase angle given by the CWT provided a useful tool for statistically detecting and reconstructing the phase relationship between the two time series of fish data. Moreover, the correlation coefficients at each lag between the water level and fish abundance identified by the CCF provided a mechanism to understand how the flow pulse influenced the dynamics of fish. The results showed that H. lobatus moved out of the floodplain and the lake earlier than H. siamensis. Herein, H. lobatus retreats slower than H. siamensis in the Tonle Sap Lake, but they retreated at similar speeds when they reach the Tonle Sap River. This suggests that the two species respond differently to the out-flow pulse only when they are inside the TSL, indicating the main role of flow pulses in determining the occurrence and movement of fish in the Tonle Sap Ecosystem. We also discussed the role and applications of these modelling approaches for linking the changes in environmental parameters and fish. The CWT and CCF, based on our results, are helpful modelling approaches for analyzing time-series data to understand the phase relationship and the periodicity of synchronisation (joint periodicity), i.e. the role of environmental factors in shaping fish occurrence and movement in a flood-pulse ecosystem.

Leak detection in water distribution pipes using singular spectrum analysis

ABSTRACTSmall leaks in buried water distribution pipelines run continuously for long periods of time without being detected. They do not produce any appreciable flow or pressure changes at the monitored locations. The non-stationarity of the monitoring data, background noise, and the uncertainties in interpreting sensory information adds complexity to detecting leaks. This paper explores the application of singular spectrum analysis (SSA) in extracting leak components from noisy measurements. SSA is a non-parametric and adaptive method, able to decompose a signal into interpretable components without making linearity or stationarity assumptions. When applied to noisy hydro-acoustic signals, it is shown that the leak signatures are extracted efficiently. A semi-supervised approach for leak detection is presented, in which the SSA decomposition of leak-free historical data is combined with ensemble one-class support vector machine. The results demonstrate the effectiveness of SSA for leak detection in water distribution pipelines.

Optimized Dickey-Fuller Test Refines Sign and Boundary Problems Compare to Traditional Dickey-Fuller Test

Impede nonstationarity is vigorous to study performance of time series data and removes long-term components to expose any regular short-term regularity. So, we find miscellaneous unit root tests for instance Dickey-Fuller test, Augmented Dickey-Fuller plus DF-GLS Tests and identify that almost all unit root tests with the estimated model suffer from sign and boundary problems of the parameters to smooth the progress of the non-stationarity problem. In this paper, we usage Dickey-Fuller test and impose some limits on the parameter. Our proposed optimized DF test based on error sum of square (ESS). Monto Carlo simulation method is used to generate simulated critical values for different sample size. Our proposed optimized DF test gives better result than the ordinary DF test with effectiveness, uniformity and power properties. Also, optimized DF improves the sign and boundary problems through imposing some limit on error sum of squares and capture more nonstationarity of time related data.

Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls.

We employ a time-dependent Hurst analysis to identify EEG signals that differentiate between healthy controls and combat-related PTSD subjects. The Hurst exponents, calculated using a rescaled range analysis, demonstrate a significant differential response between healthy and PTSD samples which may lead to diagnostic applications. To overcome the non-stationarity of EEG data, we apply an appropriate window length wherein the EEG data displays stationary behavior. We then use the Hurst exponents for each channel as hypothesis test statistics to identify differences between PTSD cases and controls. Our study included a cohort of 12 subjects with half healthy controls. The Hurst exponent of the PTSD subjects is found to be significantly smaller than the healthy controls in channel F3. Our results indicate that F3 may be a useful channel for diagnostic applications of Hurst exponents in distinguishing PTSD and healthy subjects.

An ARIMA Model With Adaptive Orders for Predicting Blood Glucose Concentrations and Hypoglycemia

The continuous glucose monitoring system is an effective tool, which enables the users to monitor their blood glucose (BG) levels. Based on the continuous glucose monitoring (CGM) data, we aim at predicting future BG levels so that appropriate actions can be taken in advance to prevent hyperglycemia or hypoglycemia. Due to the time-varying nonstationarity of CGM data, verified by Augmented Dickey-Fuller test and analysis of variance, an autoregressive integrated moving average (ARIMA) model with an adaptive identification algorithm of model orders is proposed in the prediction framework. Such identification algorithm adaptively determines the model orders and simultaneously estimates the corresponding parameters using Akaike Information Criterion and least square estimation. A case study is conducted with the CGM data of diabetics under daily living conditions to analyze the prediction performance of the proposed model together with the early hypoglycemic alarms. Results show that the proposed model outperforms the adaptive univariate model and ARIMA model.

Long-Run Causal Order: A Preliminary Investigation

The concept of long-run causal order and its relationship to nonstationarity in time series data is explored using some ideas drawn from the literature on graphical causal modeling. Ordinary variables, which are nonstationary only because they are caused by a distinct nonstationary variable, are are distinguished from fundamental trends, which are nonstationary owing to their own-dynamics. Although systems of equations in which the own dynamics appears stationary may, in principle, generate nonstationary behavior through their interaction, it is argued that such behavior is nongeneric and that typically ordinary variables trend because an (often latent) fundamental trends is among their causes. The possibility of inferring the long-run causal structure almong a set of time-series variables from an exhaustive examination of weak exogeneity in irreducibly cointegrated subsets of variables is explored and illustrated.

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption.

Oilseeds production and yield forecasting using ARIMA-ANN modelling

Agriculture, the backbone of Indian economy, lot of time series data on various parameters and these time series data generated from agriculture can be effectively modelled using various time-series modelling techniques such as Box-Jenkins ARIMA modelling technique, State-Space modelling technique, Structural Time Series modelling methodology and various other time series modelling methodologies depending upon the availability and properties of the given datasets. Modelling and forecasting of time-series data on all-India production and productivity of nine oilseed crops from 1950-51 to 2015-16 is carried out in this study. Modelling and subsequent forecasting for the datasets under consideration is performed using Autoregressive Moving Average (ARIMA), Artificial Neural Network (ANN) and ARIMA-ANN hybrid modelling methodologies. It is important to note that ARIMA is a linear modelling methodology whereas ANN is a non-linear modelling methodology. ARIMA-ANN is a hybrid of these two methodologies which can efficiently capture both linear and non-linear structures present in the dataset under consideration. For efficient performance of ARIMA modelling, the data under consideration needs to satisfy stationarity criterion. i.e. mean and variance should constant over a period of time. First, the dataset on oilseeds production and productivity are tested for stationarity and subsequent to non-stationarity of the original data, firstorder differenced series are considered for modeling using the Box-Jenkins approach. Various ARIMA models are developed and among them ARIMA (1,1,0) and ARIMA (1,1,1) are found suitable for the production andproductivity data, respectively, based on the Information Criteria such as Akaike Information Criterion (AIC) and Schwarz-Bayesian Criterion (SBC). Among the developed ANN models, the Neural Network Autoregression(NNAR) of order NNAR (2,2) is found to be suitable for both the variables under study. Later, models using ANN-ARIMA hybrid methodology are developed and the ARIMA (1,1,0)-NNAR(1,1) and ARIMA (1,1,1)-NNAR(1,1) for production and productivity are found to be suitable, respectively. All the three models are tested for their forecast accuracy using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Accordingly, the ARIMA-ANN hybrid methodology is found to be superior to the individual ARIMA and ANN methodologies. Based on the efficient developed model viz., hybrid ARIMA-ANN model, forecasting annual all-India oilseeds production for the year 2022 is carried out and is found to be 35.6 (±3.2) million tonnes with a productivity of 1178 (±94.6) kg/hectare.

The Growing Curvilinear Component Analysis (GCCA) neural network

Big high dimensional data is becoming a challenging field of research. There exist a lot of techniques which infer information. However, because of the curse of dimensionality, a necessary step is the dimensionality reduction (DR) of the information. DR can be performed by linear and nonlinear algorithms. In general, linear algorithms are faster because of less computational burden. A related problem is dealing with time-varying high dimensional data, where the time dependence is due to nonstationary data distribution. Data stream algorithms are not able to project in lower dimensional spaces. Indeed, only linear projections, like principal component analysis (PCA), are used in real time while nonlinear techniques need the whole database (offline). The Growing Curvilinear Component Analysis (GCCA) neural network addresses this problem; it has a self-organized incremental architecture adapting to the changing data distribution and performs simultaneously the data quantization and projection by using CCA, a nonlinear distance-preserving reduction technique. This is achieved by introducing the idea of “seed”, pair of neurons which colonize the input domain, and “bridge”, a novel kind of edge in the manifold graph, which signals the data non-stationarity. Some artificial examples and a real application are given, with a comparison with other existing techniques.

Comparative study of chaotic features in hourly wind speed using recurrence quantification analysis

Due to the shortage in electricity supply in Nigeria, there is a need to improve the alternative power generation from wind energy by analysing the wind speed data available in some parts of the country, for a better understanding of its underlying dynamics for the purpose of good prediction and modelling. The wind speed data used in this study were collected over a period of two years by National Space Research and Development Agency (NASRDA) from five different stations in the tropics namely; Abuja (7050'02.09"N and 6004'29.97"E), Akungba (6059'05.40"N and 5035'52.23"E), Nsukka (6051'28.14"N and 7024'28.15"E), Port Harcourt (4047'05.41"N and 6059'30.62"E), and Yola (9017'33.58"N and 12023'26.69"E). In this paper, recurrence plot (RP) and recurrence quantification analysis (RQA) are applied to investigate a non-linear deterministic dynamical process and non-stationarity in hourly wind speed data from the study areas. Using RQA for each month of the two years, it is observed that wind speed data for the wet months exhibit higher chaoticity than that of the dry months for all the stations, due to strong and weak monsoonal effect during the wet and dry seasons respectively. The results show that recurrence techniques are able to identify areas and periods for which the harvest of wind energy for power generation is good (high predictability) and poor (low predictability) in the study areas. This work also validates the RQA measures (Lmax, DET and ENT) used and establishes that they are similar/related as they give similar results for the dynamical characterization of the wind speed data.

Towards an East German wage curve - NUTS boundaries, labour market regions and unemployment spillovers

The relevance of spatial effects in the wage curve can be rationalized by the model of monopsonistic competition in regional labour markets. However, distortions in extracting the regional unemployment effects arise for administrative boundaries at the district level as they fail to adequately capture spatial processes. In addition, the nonstationarity of wages and unemployment is often ignored. Both issues are particularly important in high unemployment regimes like East Germany where a wage curve is difficult to establish. In this paper, labour market regions defined by economic criteria are used to examine the existence of an East German wage curve. Due to the nonstationarity of spatial data, a global panel cointegration approach is adopted. By specifying a spatial error correction model (SpECM), equilibrium adjustments are investigated in time and space. The analysis gives evidence on a locally but not a spatially cointegrated wage curve for East Germany.

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

Estimation of the Magnetotelluric Response Function: The Path from Robust Estimation to a Stable Maximum Likelihood Estimator

The robust statistical model of a Gaussian core contaminated by outlying data in use since the 1980s, and which underlies modern estimation of the magnetotelluric (MT) response function, is re-examined from first principles. The residuals from robust estimators applied to MT data are shown to be systematically long-tailed compared to a distribution based on the Gaussian and hence inconsistent with the robust model. Instead, MT data are pervasively described by the stable distribution family for which the Gaussian is an end member, but whose remaining distributions have algebraic rather than exponential tails. The validity of the stable model is rigorously demonstrated using a permutation test. A maximum likelihood estimator (MLE), including the use of a remote reference, that exploits the stable nature of MT data is formulated, and its two-stage implementation, in which stable parameters are first fit to the residuals, and then the MT responses are solved for, with iteration between them, is described. The MLE is inherently robust, but differs from a conventional robust estimator because it is based on a statistical model derived from the data rather than being ad hoc. Finally, the covariance matrices obtained from MT data are pervasively improper as a result of weak non-stationarity, and the Cramer–Rao lower bound for the improper covariance matrix is derived, resulting in reliable second-order statistics for MT responses. The stable MLE was applied to an exemplar broadband data set from northwest Namibia. The stable MLE is shown to be consistent with the statistical model underlying linear regression and hence is unconditionally unbiased, in contrast to the robust model. The MLE is compared to conventional robust remote reference and two-stage estimators, establishing that the standard errors of the former are systematically smaller than for either of the latter, and that the standardized differences between them exhibit excursions that are both too frequent and too large to be described by Gaussian statistics. These excursions are more prevalent when the tail thickness parameter of the stable distribution is small, and are attributed to rising bias in the robust estimator that is also consistent with the Berry–Esseen theorem that defines the rate of convergence to the central limit theorem value. An explanation for weak non-stationarity of MT data is proposed, and several extensions to the present work are described.

Local and spatial cointegration in the wage curve – a spatial panel analysis for german regions

The wage curve introduced by Blanchflower and Oswald (1990, 1994b) postulates a negative correlation between wages and unemployment. Empirical studies use different channels for a theoretical underpinning the relationship. Panel data models mostly draw on bargaining power or the efficiency wage hypothesis. Spatial econometric approaches can be rationalized by monopsonistic competition. However, the approaches either ignore the issue of nonstationarity or treat the data as if it were nonspatial. In this paper, we adopt a global cointegration approach to account for nonstationarity of regional data. By specifying a spatial error correction model (SpECM), equilibrium adjustments are considered in both time and space. For West Germany, strong evidence for the existence of a long-run wage curve with spatial effects is found.

Climate change effects and extreme rainfall non-stationarity

It is necessary to perform analysis to confirm stationarity or detect non-stationarity of extreme rainfall data in order to derive accurate design rainfall estimates for hydraulic structure design. In this study, non-stationarity of extreme rainfalls in the Antalya region in Turkey was investigated through statistical non-stationarity tests and stationary and non-stationary generalised extreme value distribution models. After determination of stationarity of extreme rainfalls in the Antalya region, a frequency analysis was conducted in a stationary context to derive design rainfall intensities. Future (2080–2099) design rainfall intensities were then estimated and bias corrected, and current and future design rainfall intensities were compared and discussed. Non-stationarity analysis showed no evidence of non-stationarity in Antalya. Bias-corrected future design rainfall intensity estimates showed that design rainfall intensities in the future are expected to be larger than current design rainfall intensities for return periods of 5, 10, 20, 50 and 100 years. This study is expected to contribute to a better understanding of the relationship between global warming and extreme rainfall in one of the most climate change sensitive parts of Turkey.

Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine

Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS). However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management. A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM) is proposed. Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO) algorithm. To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods. Experimental results show that QPSO-MKELM is more effective for practical applications. And it will help traffic managers to make right decisions.

Towards an East German Wage Curve - Nuts Boundaries, Labour Market Regions and Unemployment Spillovers

The relevance of spatial effects in the wage curve can be rationalized by the model of monopsonistic competition in regional labour markets. However, distortions in extracting the regional unemployment effects arise in standard regional (i.e. NUTS) classifications as they fail to adequately capture spatial processes. In addition, the nonstationarity of wages and unemployment is often ignored. Both issues are particularly important in high unemployment regimes like East Germany where a wage curve is difficult to establish. In this paper, labour market regions defined by economic criteria are used to examine the existence of an East German wage curve. Due to the nonstationarity of spatial data, a global panel cointegration approach is adopted. By specifying a spatial error correction model (SpECM), equilibrium adjustments are investigated in time and space. The analysis gives evidence on a locally but not a spatially cointegrated wage curve for East Germany.