Study of Saudi Arabian climatic conditions using Hurst exponent and climatic predictability index

This paper utilizes Hurst exponent to study the persistency of meteorological parameters individually and dependency of rainfall/precipitation on pressure and temperature using climate predictability index. For the purpose, daily averages of surface pressure and temperature and daily total rainfall data for a period of 7 years for three locations and 14 years for seven locations has been utilized. The Hurst exponents (H) for above mentioned meteorological parameters were calculated using rescaled range analysis (R/S) and absolute moments methods. These H values were used to calculate the fractal dimension D for pressure, temperature and rainfall data. Finally, these D’s were used to calculate the climate predictability index PI_C in terms of pressure predictability index (PI_P), temperature predictability index (PI_T) and rainfall predictability index (PI_R). The Hurst exponent analysis showed that H values for rainfall, relative humidity and wind speed time series data for all the stations were >0.5 which is indicative of persistence behavior of the parameters on the previous values while for pressure and temperature H values were <0.5 means anti-persistence behavior. The climate predictability index showed that in most of the cases the rainfall was dependent on both pressure and temperature predictability indices. In some cases it was more dependent on pressure index than the temperature and in some cases otherwise. In Saudi Arabia there is no prevalent or established rainy season and the present analysis could not result into concrete results. It is therefore recommended to analyze the data by breaking the entire data set into seasons and further into different years.     

A spatial rainfall simulator for crop production modeling in Southern Africa

This paper describes a methodology for simulating rainfall in dekads across a set of spatial units in areas where long-term meteorological records are available for a small number of sites only. The work forms part of a larger simulation model of the food system in a district of Zimbabwe, which includes a crop production component for yields of maize, small grains and groundnuts. Only a limited number of meteorological stations are available within or surrounding the district that have long time series of rainfall records. Preliminary analysis of rainfall data for these stations suggested that intra-seasonal temporal correlation was negligible, but that rainfall at any given station was correlated with rainfall at neighbouring stations. This spatial correlation structure can be modeled using a multivariate normal distribution consisting of 30 related variables, representing dekadly rainfall in each of the 30 wards. For each ward, log-transformed rainfall for each of the 36 dekads in the year was characterized by a mean and standard deviation, which were interpolated from surrounding meteorological stations. A covariance matrix derived from a distance measure was then used to represent the spatial correlation between wards. Sets of random numbers were then drawn from this distribution to simulate rainfall across the wards in any given dekad. Cross-validation of estimated rainfall parameters against observed parameters for the one meteorological station within the district suggests that the interpolation process works well. The methodology developed is useful in situations where long-term climatic records are scarce and where rainfall shows pronounced spatial correlation, but negligible temporal correlation.     

时间序列中方差的结构变点的小波识别(英文)

本文给出了时间序列中方差的小波系数的两种估计:连续估计和离散估计.这两种估计可以用来检测时间序列中方差的结构变点.利用这两种估计我们给出了方差变点的位置和跳跃幅度的估计,并且显示出这些估计可达到最佳收敛速度.同时,我们还给出了这些估计的收敛速度以及检验统计量的渐进分布!     

A 3D hydrodynamic numerical model of the Río de la Plata and Montevideo’s coastal zone

The 3D hydrodynamic numerical model MOHID was applied in the Río de la Plata and Montevideo coastal zone in order to represent the main dynamics and to study its complex circulation pattern. The hydrodynamic model was calibrated and validated considering the following main forces: fresh water flow, astronomical and meteorological tides in the oceanic boundary, and wind acting on the water surface. A series of water levels measured at six coastal stations and vertical profiles of current velocity measured at four different locations in the estuarine zone of the Río de la Plata were used for calibrating and validating the hydrodynamic model. The calibration process was carried out in two steps. First the astronomical waves propagation was calibrated comparing harmonic constants of observed and computed sea surface elevation data. Next, both the astronomical and meteorological wave propagation was calibrated. Direct comparison of scatter plot and root-mean square errors of model results and field data were used when evaluating the calibration quality. The calibrated model shows good agreement with the measured water surface level in the entire domain with mean error values being minor than 20% of the measured data and correlation factors higher than 0.74. Also, the intensity and velocity direction observed in the currents data are well represented by the model in both bottom and surface levels with errors similar to 30% of the currents data components. Using the 3D calibrated model the bottom and surface residual circulation for a four month period of time was analyzed.     

Bayesian Methods for Wavelet Series in Single-Index Models

Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. This article proposes a nonparametric estimation approach that combines wavelet methods for nonequispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods.     

Scale space multiresolution correlation analysis for time series data

We propose a new scale space method for the discovery of structure in the correlation between two time series. The method considers the possibility that correlation may not be constant in time and that it might have different features when viewed at different time scales. The time series are first decomposed into additive components corresponding to their features in different time scales. Temporal changes in correlation between pairs of such components are then explored by using weighted correlation within a sliding time window of varying length. Bayesian, sampling-based inference is used to establish the credibility of the correlation structures thus found and the results of analyses are summarized in scale space feature maps. The performance of the method is demonstrated using one artificial and two real data sets. The results underline the usefulness of the scale space approach when the correlation between the time series exhibit time-varying structure in different scales.     

Analysis of temperature time-series: Embedding dynamics into the MDS method

Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations’ locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.     

Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm

This paper presents a novel four-stage algorithm for the measurement of the rank correlation coefficients between pairwise financial time series. In first stage returns of financial time series are fitted as skewed-t distributions by the generalized autoregressive conditional heteroscedasticity model. In the second stage, the joint probability density function (PDF) of the fitted skewed-t distributions is computed using the symmetrized Joe–Clayton copula. The joint PDF is then utilized as the scoring scheme for pairwise sequence alignment in the third stage. After solving the optimal sequence alignment problem using the dynamic programming method, we obtain the aligned pairs of the series. Finally, we compute the rank correlation coefficients of the aligned pairs in the fourth stage. To the best of our knowledge, the proposed algorithm is the first to use a sequence alignment technique to pair numerical financial time series directly, without initially transforming numerical values into symbols. Using practical financial data, the experiments illustrate the method and demonstrate the advantages of the proposed algorithm.     

Almost everywhere convergence of a wavelet thresholding risk estimate in a model with correlated noise

The problem of estimating a function in a model with correlated noise by thresholding its wavelet decomposition coefficients is considered. The risk estimate using this approach is shown to be strongly consistent.     

Stochastic expansions in an overcomplete wavelet dictionary

We consider random functions defined in terms of members of an overcomplete wavelet dictionary. The function is modelled as a sum of wavelet components at arbitrary positions and scales where the locations of the wavelet components and the magnitudes of their coefficients are chosen with respect to a marked Poisson process model. The relationships between the parameters of the model and the parameters of those Besov spaces within which realizations will fall are investigated. The models allow functions with specified regularity properties to be generated. They can potentially be used as priors in a Bayesian approach to curve estimation, extending current standard wavelet methods to be free from the dyadic positions and scales of the basis functions. Received: 21 September 1998 / Revised version: 20 August 1999 / Published online: 30 March 2000     

沪深港股市相关性的小波分析

主要使用离散小波变换(DW T)对沪深港股市的相关性进行研究.小波可以把方差和相关系数在不同尺度上进行分解,以便更仔细地研究时间序列的波动性在不同尺度上的相关程度.研究发现:三地股票市场的波动性都随着小波尺度的变化而变化;沪深股市与香港股市相关性非常低,而且在不同尺度上相关程度有较大差别.     

Effect of localized input on bump solutions in a two-population neural-field model

We investigate a two-population Wilson–Cowan model extended with stationary and spatially dependent localized external inputs and study the existence and stability of localized stationary (bump) solutions. The generic situation for this model in the absence of external inputs corresponds to two bump pairs, one narrow and one broad pair. For spatially wide localized external inputs we find this generic picture to be unchanged. However, for strongly localized external inputs we find that three and even four bump pairs, all with symmetric activity profiles around the center of the localized external input, may coexist. We next investigate the stability of these bump pairs using two different techniques: a simplified phase–space reduction (Amari) technique and full stability analysis. Examples of models, i.e., choices of model parameters, exhibiting up to three stable bump pairs are found. The Amari technique is further found to be a poor predictor of stability in the case of strongly localized external inputs. The bump-pair states are also probed numerically using a fourth order Runge–Kutta method, and an excellent agreement is found between numerical simulations and the analytical predictions from full stability analysis.     

两组变量间相关关系的统计分析方法

研究典型相关分析的原理、典型成分的计算方法及计算步骤.把两组变量X与Y转化为具有最大相关性的若干对典型成分,直到两组变量的相关性被分解.通过典型相关系数及其显著性检验.选择典型成分分析两组变量的相关性.实例表明只有第一个典型相关系数能通过显著性检验,而其它两个典型相关系数显著为零,故应选取第一对典型成分F1和G1做分析...     

面向COVID-19传播模式的多因素影响分析

目前,新型冠状病毒肺炎(COVID-19)的传播仍在持续,其传播模式以及影响传播行为的主要因素仍有待深入挖掘。鉴于此,本文从数据分析的角度,通过构造一个特殊的多源数据集(包括COVID-19历史数据、气象数据、人口迁徙数据和空间地理信息数据),以此建立多元Poisson.回归模型(类Poisson回归)来着重分析国内疫情的病毒传播模式及其影响因素。分析结果显示,湿度、平均每日风速、每日的降雨量等气象因素与COVID-19的传播模式显著相关,但与每日温度变化显著不相关。除此之外,COVID-19的传播速度及传播范围与武汉迁出目的地的人口比例、迁入武汉来源地的人口比例以及武汉与其他城市的空间距离均有一定的关联性。全文可视化及模型分析的R代码见:https://github.com/thwgithub/COVID-19 Rcodes.     

A useful reparameterization for second‐order autoregressive processes

In time series analysis, autoregressive (a.r.) models are often fitted successfully to data, and such models are usually among the first to be taught. It is therefore important to understand why such models are useful in practice. An alternative parameterization for second order a.r. models, which assists understanding and interpretation, is discussed. The models are most plausible if certain restrictions are placed on the autoregressive parameters, which in turn lead to restrictions on the autocorrelation coefficients. Fitting the reparameterized model is straightforward, and the reparameterization may be extended, less usefully, to higher order a.r. models. The second‐order model is fitted to some meteorological data.     

Selecting the Superpositioning of Models for Railway Freight Forecasting

The problem of selecting the optimum system of models for forecasting short-term railway traffic volumes is considered. The historical data is the daily volume of railway traffic between pairs of stations for different types of cargo. The given time series are highly volatile, noisy, and nonstationary. A system is proposed that selects the optimum superpositioning of forecasting models with respect to features of the historical data. A model of sliding averages, exponential and kernel-smoothing models, the ARIMA model, Croston’s method, and LSTM neural networks are considered as candidates for inclusion in superpositioning.     

A Kendall correlation coefficient between functional data

Measuring dependence is a very important tool to analyze pairs of functional data. The coefficients currently available to quantify association between two sets of curves show a non robust behavior under the presence of outliers. We propose a new robust numerical measure of association for bivariate functional data. We extend in this paper Kendall coefficient for finite dimensional observations to the functional setting. We also study its statistical properties. An extensive simulation study shows the good behavior of this new measure for different types of functional data. Moreover, we apply it to establish association for real data, including microarrays time series in genetics.

     

Multi–regime models for nonlinear nonstationary time series

Nonlinear nonstationary models for time series are considered, where the series is generated from an autoregressive equation whose coefficients change both according to time and the delayed values of the series itself, switching between several regimes. The transition from one regime to the next one may be discontinuous (self-exciting threshold model), smooth (smooth transition model) or continuous linear (piecewise linear threshold model). A genetic algorithm for identifying and estimating such models is proposed, and its behavior is evaluated through a simulation study and application to temperature data and a financial index.     

某场站温度预报初探

本文基于某场站温度变化的特点,对温度数据进行统计分析,利用平稳时间序列的理论就其数据建立模型进而做出预报,并验证此理论方法在场站温度预报中是有效可行的.