Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

Hydrodynamic characteristics of a typical karst spring system based on time series analysis in northern China

In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spring and Heihu Spring in Jinan karst spring system,a typical karst spring system in northern China.Results show that the auto-correlation coefficient of spring water level reaches the value of 0.2 after 123 days and 117 days for Baotu Spring and Heihu Spring,respectively.The regulation time obtained from the simple spectral density function in the same period is 187 days and 175 days for Baotu Spring and Heihu Spring.The auto-correlation coefficient of spring water level reaches the value of 0.2 in 34-82 days,and regulation time ranges among 40-59 days for every single hydrological year.The delay time between precipitation and spring water level obtained from cross correlation function is around 56 days for the period of 2012-2019,and varies among 30-79 days for every single hydrological year.In addition,the spectral bands in cross amplitude functions and gain functions are small with 0.02,and the values in the coherence functions are small.All these behaviors illustrate that Jinan karst spring system has a strong memory effect,large storage capacity,noticeable regulation effect,and time series analysis is a useful tool for studying the hydrodynamic characteristics of karst spring system in northern China.

Time-Series Data and Analysis Software of Connected Vehicles

In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.

Using time series analysis to assess tidal effect on coastal groundwater level in Southern Laizhou Bay, China

Sea water intrusion is an environmental problem cause by the irrational exploitation of coastal groundwater resources and has attracted the attention of many coastal countries.In this study,we used time series monitoring data of groundwater levels and tidal waves to analyze the influence of tide flow on groundwater dynamics in the southern Laizhou Bay.The auto-correlation and cross-correlation coefficients between groundwater level and tidal wave level were calculated specifically to measure the boundary conditions along the coastline.In addition,spectrum analysis was employed to assess the periodicity and hysteresis of various tide and groundwater level fluctuations.The results of time series analysis show that groundwater level fluctuation is noticeably influenced by tides,but the influence is limited to a certain distance and cannot reach the saltwater-freshwater interface in the southern Laizhou Bay.There are three main periodic components of groundwater level in tidal effect range(i.e.23.804 h,12.500 h and 12.046 h),the pattern of which is the same as the tides.The affected groundwater level fluctuations lag behind the tides.The dynamic analysis of groundwater indicates that the coastal aquifer has a hydraulic connection with seawater but not in a direct way.Owing to the existence of the groundwater mound between the salty groundwater(brine)and fresh groundwater,the maximum influencing distance of the tide on the groundwater is 8.85 km.Considering that the fresh-saline groundwater interface is about 30 km away from the coastline,modern seawater has a limited contribution to sea-salt water intrusion in Laizhou Bay.The results of this study are expected to provide a reference for the study on sea water intrusion.

Comparison study of typical algorithms for reconstructing time series from the recurrence plot of dynamical systems

The three most widely used methods for reconstructing the underlying time series via the recurrence plots （RPs） of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a periodical series, a random series, and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis. The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm. It is verified that, based on the unthresholded RPs, one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm. It is shown that, in real applications, it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems. Moreover, rules of the threshold chosen in the algorithm are also suggested.

Feature Selection for Time Series Modeling

In machine learning, selecting useful features and rejecting redundant features is the prerequisite for better modeling and prediction. In this paper, we first study representative feature selection methods based on correlation analysis, and demonstrate that they do not work well for time series though they can work well for static systems. Then, theoretical analysis for linear time series is carried out to show why they fail. Based on these observations, we propose a new correlation-based feature selection method. Our main idea is that the features highly correlated with progressive response while lowly correlated with other features should be selected, and for groups of selected features with similar residuals, the one with a smaller number of features should be selected. For linear and nonlinear time series, the proposed method yields high accuracy in both feature selection and feature rejection.

基于改进灰狼优化与支持向量回归的滑坡位移预测

针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。

大九湖泥炭藓沼泽植被指数时空变化研究

基于Landsat系列卫星数据,计算大九湖泥炭藓沼泽地区1986-2021年的归一化植被指数(NDVI),利用Theil-Sen median趋势分析和Mann-Kendall显著性检验方法,结合研究区气象资料,分析近36 a来植被指数的时空演变规律。结果表明:①植被指数变化在2005年存在明显的拐点。1986-2005年总体呈退化趋势,显著和微显著退化的面积占比约60.38%;2006-2021年则总体呈改善趋势,显著和微显著改善的面积占比约81.5%。②泥炭藓斑块与沼泽区域植被指数的变化基本一致,但前期退化的幅度明显高于后期的改善幅度。③研究区植被指数相比气温和降水存在明显的滞后性,NDVI滞后于气温一个月,滞后于降水3个月。④1986-2020年,研究区NDVI对降水的响应更稳定,且集中在中部泥炭藓覆盖度较高区域,这与泥炭藓植被的生态特性基本一致。

物联网环境下光纤通信网络总线链路丢包率推断

光纤通信网络总线链路在应用过程中容易受到带宽限制,导致光纤通信网络链路丢包率检测精度差、推断效率低,不能满足用户数据传输需求。为此,提出物联网环境下光纤通信网络总线链路丢包率推断方法。根据光纤通信网络路由矩阵,获取物联网环境下光纤网络的通信状态,依据时序分析策略检测网络总线链路数据传输中的薄弱环节,分析不同时段间变量的关联性,建立线性回归模型,计算下个时段预测值,获取总线链路传输状态。计算每条线路的时空关联性,结合背景流量,采用最小二乘法推断链路丢包率。实验结果表明,所提方法的故障识别率高于95%,误差因子与相对误差在0.2和0.02以下,推断耗时低于2 ms,为光纤通信网络的数据传输提供参考。

时序分析下经济增长对金融衍生产品需求影响研究

为深入研究经济增长对金融衍生产品需求的影响,给金融市场参与者和投资者提供有效支持,促进金融市场的稳定发展。首先考虑了金融市场的多方面因素,建立了自回归、混合自回归和移动回归的时序分析模型。然后深入剖析了金融衍生产品的基本特征,选取了相关变量进行解释。最后说明了金融衍生产品的基本功能,了解其在经济增长下的作用。实证结果显示,非金融机构衍生产品对企业价值影响的相关系数为0.204 9,企业经济增长对衍生品需求的影响为0.000 3。通过时序分析,有助于揭示经济周期、经济波动等因素对金融衍生产品市场的影响机制,为投资者和参与者提供有效支持。

Interval Sampling Method for Steady-State Simulation Output Analysis

A new algorithm namely the interval sampling method, applicable to the analysisof steady-state simulation output is proposed. This algorithm uses the time series analysisto carry out conrrelation analysis of the steady-state simulation output so as to obtain theobservation data which are actually uncorrelated in nature. On the basis of theseuncorrelated data gathered, some satisfactory deductions cam be made on the data under re search. A comparison between batch means method and the interval sampling method hasbeen performed by taking the M/M/l queuing system as an example. The results attestedthat the interval sampling method is mere accurate than the batch means method.

Age Dependence of the Menstrual Cycle Correlation Dimension

Time series analysis, based on the idea that female reproductive endocrine physiology can be construed as a nonlinear dynamical system in a chaotic trajectory, is performed to measure the correlation dimension of the menstrual cycle data from subjects in two different age cohorts. The dimension is computed using a method proposed by Judd (Physica D, vol. 56, 1992, pp. 216-228) that does not assume the correlation dimension to be necessarily constant for all appropriate time scales of the system’s strange attractor. Significant time scale differences are found in the behavior of the dimension between the two age cohorts, but at the shortest time scales the correlation dimension converges to the same value, approximately 5.5, in both cases.

基于数据关联性分析的工业用户电能质量特征识别

随着电力电子型用电设备的大量使用,电网公共连接点上干扰源用户的类型和数量越发增多,使得其电能质量扰动指标特征和时空特征更为复杂。针对目前电能质量监测装置难以对每条馈线上各终端用户均实现专门监测,且无法识别馈线上多个用户各自电能质量特征等问题,提出一种基于多源数据关联分析的工业用户电能质量特征识别方法。首先,以指标限值和累积分布图拐点为依据,提取监测点电能质量时间序列的超标和波动的数值及其时段,得到谐波关键指标、电压偏差、负序电压不平衡度等指标的扰动时序数据;其次,提出一种基于导数动态时间弯曲的时序距离计算方法,分析扰动时段下监测指标与工业用户用电数据的相关性,依据不同指标关联度识别用户的电能质量特征;最后,基于多类型干扰源仿真和实测算例,验证所提方法的可行性,可实现不同工业用户的电能质量特征识别。

Regional Economic Vitality Based on Weighted Grey Relational Analysis

The future development of cities has a great relationship with economic vitality.To determine the size of the economic vitality and its main influencing factors.This article takes some cities in China as examples.First,determine the main factors.Aiming at many factors,this paper starts from the perspective of population changes in different cities and changes in corporate vitality.After applying the rough set theory to objectively evaluate index weights,the main factors are screened out.Then,the weights of the corresponding evaluation indexes of each group of cities are calculated by a multiple linear regression to a weighted index system,and then the cities are ranked using the gray correlation analysis method.Finally,we get the ranking of the economic vitality level of different cities.Finally,suggestions are made based on the weighting factors of major factors and economic vitality.

The Research of Fractal Characteristics of the Electrocardiogram in a Real Time Mode

The article presents the results of recent investigations into Holter monitoring of ECG, using non-linear analysis methods. This paper discusses one of the modern methods of time series analysis--a method of deterministic chaos theory. It involves the transition from study of the characteristics of the signal to the investigation of metric （and probabilistic） properties of the reconstructed attractor of the signal. It is shown that one of the most precise characteristics of the functional state of biological systems is the dynamical trend of correlation dimension and entropy of the reconstructed attractor. On the basis of this it is suggested that a complex programming apparatus be created for calculating these characteristics on line. A similar programming product is being created now with the support of RFBR. The first results of the working program, its adjustment, and further development, are also considered in the article.

Multi-factor Comprehensive Prediction of Delay Time through Congested Road Sections

The navigation software uses the positioning system to determine the traffic conditions of the road sections in advance,so as to predict the travel time of the road sections.However,in the case of traffic congestion,the accuracy of its prediction time is low.After empirical analysis,this paper establishes a multi-factor synthesis by studying 7 factors:traffic flow,number of stops,traffic light duration,road network density,average speed,road area,and number of intersections the prediction function achieves the purpose of accurately predicting the transit time of congested road sections.The gray correlation coefficients of the seven factors obtained from the gray correlation analysis are:0.9827,0.9679,0.6747,0.8030,0.9445,0.8759,0.4328.The correlation coefficients of traffic volume,number of stops,average speed,and road congestion delay time were all about 95%,which were the main influencing factors of the study.The prediction needs to be based on functions.This paper fits the main influencing factors to the delay time of congested roads.It is found that the delay time varies parabolically with the traffic flow and the number of stops,and linearly with the average speed.Because the three impact factors have different weights on the delay time of congested roads,demand takes the weight of each factor.Therefore,the gray correlation coefficients occupied by the main influencing factors are normalized to obtain the weights of three of 0.340,0.334,and 0.326.The weighted fitting function is subjected to nonlinear summation processing to obtain a multi-factor comprehensive prediction function.By comparing the original data with the fitting data and calculating the accuracy of the fitting function,it is found that the accuracy of each fitting function is close to 0,the residual error,the relative error is small,and the accuracy is high.

基于时空信息转换方程的药品销量预测模型

针对药品销售中高维短时间序列预测问题,利用时空信息转换方程及储备池计算方法构建了一种基于时空信息(STI)转换方程的药品销售量预测模型。首先针对药品销售时间序列数据样本量较小的特点,引入储备池计算方法拓展数据样本信息维度,将多个不同药品销售量时序数据中的动力学信息引入储备池。使用时空信息转换方程对时间信息与空间信息进行转化,最后在储备池运算的基础上对时空信息转化方程求解,对目标药品的销售量进行有效的时间序列预测。通过将提出的基于时空信息转换方程的时序预测模型与神经网络预测模型在特定药品销售数据集进行时序预测验证并进行横向对比,相较于GRU(Gated Recurrent Unit),所提模型在测试时间节点上的均方根误差(MSE)及运算时间分别减小了13.27%和95.60%、皮尔逊相关系数提高了34个百分点;相较于长短期记忆模型(LSTM),所提模型在测试时间节点上的均方根误差及运算时间分别减小了69.85%和98.00%,而皮尔逊相关系数提高了44个百分点;相较于卷积神经网络模型(CNN),在测试节点的均方根误差及运算时间分别减少了48.96%和88.53%,皮尔逊相关系数提高了33个百分点。证明了基于时空信息转换方程的药品销售预测模型在测试集时间节点上的预测效果要优于GRU、LSTM、CNN时序预测模型,同时也说明模型具有更高的运算效率。

基于时间序列相似性分析探讨中风方剂中的症—药演变规律

【目的】基于中医古籍智能检索平台,采用时间序列相似性分析方法探索治疗中风方剂的症-药演变规律,为中风的临床诊疗提供参考。【方法】筛选治疗中风的方剂,对中风相关症状与治疗中风方剂的药物进行提取与标准化,依据方剂所属古籍的成书或刊刻时间,形成时间序列数据,依据形态相似距离评估不同历史时期的症-症、药-药、症-药间相似性,从而探寻治疗中风方剂中症-药的演变规律。【结果】共收集公元341-1949年间的2065首治疗中风的方剂,涉及中医古籍258本。症-症相关性分析得到3类有关中风的症状群;全时期的症-药时间序列中共记录中药643味,频次居前100位的中药可聚为4类中药群。症-药相关性分析结果显示症状群Ⅰ(真中风症状群)与中药群Ⅰ相关性最显著,症状群Ⅱ(类中风症状群)与中药群Ⅱ相关性最显著,症状群Ⅲ(急中风症状群)与中药群Ⅰ相关性最显著。【结论】通过中风症-药时间序列相似性分析发现,中风相关症状逐渐形成真中风、类中风与急中风3组症状群,与其密切相关的中药可分为4类,各症状群与各中药群关系迥异,其中药配伍日益丰富,治法组合也逐渐完善。挖掘所得结果可为中医药治疗中风的理论发展和临床应用提供一定的参考。

液态金属电池串联成组的一致性分选

液态金属电池是一种兼具低成本、长寿命、大容量优势的规模储能技术。为提高串联液态金属电池组一致性和可靠性,该文提出一种电池综合分选方法。基于单体电池的活化数据,获取液态金属电池的综合特性参数;然后,通过皮尔逊相关性分析,选取若干典型静态特性指标作为聚类特征,进行电池初分选;最后,以动态时间规整相似度对电池放电曲线的差异性进行量化评估,并采用减法聚类进行电池再分选。研究结果表明,分选后液态金属电池组容量提高了6.77%,电池放电曲线差异减少了88.28%,电池组一致性得到显著提高。

基于WOA-LSTM的工作面瓦斯涌出量预测研究

为了提高瓦斯涌出量预测的科学性和准确性,提出一种基于鲸鱼优化算法(WOA)和长短期记忆网络(LSTM)的瓦斯涌出量多步预测模型。该模型首先采用皮尔逊(Pearson)相关系数法进行瓦斯涌出量影响因素的特征分析,筛选了9个主要影响瓦斯涌出量变化的特征作为模型的外部输入特征;其次采用鲸鱼优化算法对LSTM神经网络的隐藏层神经元个数、时间步长、批处理数进行优化;最后,构建WOA-LSTM模型进行瓦斯涌出量预测,实验研究了不同时间步长下模型的预测精度并对比分析了LSTM、RNN、BP模型的预测效果。结果表明:基于WOA-LSTM的瓦斯涌出量多步预测模型在3个时间步长的预测模型误差值达到最小,其平均绝对误差相较于LSTM、RNN和BP神经网络模型分别降低了41.6%、46.6%、65.8%,具有较强的鲁棒性,可为矿井瓦斯的防治提供参考。