Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

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.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Identification and classification of transient pulses observed in magnetometer array data by time-domain principal component analysis filtering

A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these ＂training events＂ are represented in matrix form and transpose-multiplied to generate time- domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window （approxi- mately the width of the training event） is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigen- vectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled （50 Hz） data from six observatories, each equipped with three- component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.

Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appear in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid- or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

A Nonlinear Autoregressive Scheme for Time Series Prediction via Artificial Neural Networks

This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden layer. As a training algorithm we use scaled conjugate gradient (SCG) method and the Bayesian regularization (BReg) method. The first method is applied to time series without noise, while the second one can also be applied for noisy datasets. We apply the suggested scheme for prediction of time series arising in oil and gas pricing using 50 and 100 past values. Results of numerical simulations are presented and discussed.

DAViS:a unified solution for data collection, analyzation,and visualization in real‑time stock market prediction

The explosion of online information with the recent advent of digital technology in information processing,information storing,information sharing,natural language processing,and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content.For example,a typical stock market investor reads the news,explores market sentiment,and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock.However,capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market.Although existing studies have attempted to enhance stock prediction,few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making.To address the above challenge,we propose a unified solution for data collection,analysis,and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles,social media,and company technical information.We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices.Specifically,we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices.Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93.Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance.Finally,our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

Addressing the Challenge of Interpreting Microclimatic Weather Data Collected from Urban Sites

This paper presents some installation and data analysis issues from an ongoing urban air temperature and humidity measurement campaign in Hangzhou and Ningbo, China. The location of the measurement sites, the positioning of the sensors and the harsh conditions in an urban environment can result in missing values and observations that are unrepresentative of the local urban microclimate. Missing data and erroneous values in micro-scale weather time series can produce bias in the data analysis, false correlations and wrong conclusions when deriving the specific local weather patterns. A methodology is presented for the identification of values that could be false and for determining whether these are “noise”. Seven statistical methods were evaluated in their performance for replacing missing and erroneous values in urban weather time series. The two methods that proposed replacement with the mean values from sensors in locations with a Sky View Factor similar to that of the target sensor and the sensors closest to the target’s location performed well for all Day-Night and Cold-Warm days scenarios. However, during night time in warm weather the replacement with the mean values for air temperature of the nearest locations outperformed all other methods. The results give some initial evidence of the distinctive urban microclimate development in time and space under different regional weather forcings.

间歇性时间序列数据多目标决策挖掘算法设计

时序数据规模化存储能大幅度提升经济效益,但传统挖掘算法无法从海量数据中提取有效信息。为解决上述难题,通过对数据进行优化处理,提出一种MAD-SVR时序数据回归预测算法。算法首先对大数据进行标准化处理,并通过极值点分析与剔除,提升数据的有效性;然后采用多目标MIC相关性分析方法,提高对标准时序数据的间歇性特征提取能力;接着利用AHP层次分析量化指标,获取最优簇N,并基于DIANA算法完成时序数据聚类优化过程;最后通过十折交叉验证的方式,构建SVR时序数据回归预测模型,完成预测结果输出。不同叠加模型的仿真对比结果表明,较其他模型相比,MADSVR模型的MAPE参数整体减少了53.89%,R^(2)参数增加了5.99%,且RMSE参数至少下降了12.31%,即其该模型的拟合度最高,预测能力最优,且预测真实占比有较大提高,但预测真实占比误差偏离度尚有优化空间。综上,MAD-SVR算法在海量时序数据挖掘中具有重要的仿真研究价值。

基于谱域超图卷积网络的交通流预测模型

针对传统图结构难以对节点间的隐含复杂关联关系建模的问题,利用超图对交通流数据进行高阶表示,提出基于谱域超图卷积网络的交通流预测方法。首先,通过动态超边刻画数据特征层面的关系,利用谱域超图卷积,包括基于傅里叶和图小波的超图卷积及门控时序卷积,在多尺度上提取交通流的时空特征,实现端到端的节点级交通流预测。然后,采用北京市以及美国加利福尼亚州真实历史数据集进行预测实验。消融实验通过孤立和重构网络模型验证了所提方法的有效性。全时段和早高峰交通流预测的实验结果表明,该方法预测准确率高于目前主流交通流预测模型。

基于迁移学习的交互时序数据可视化生成方法

为了解决时序数据间分布不一致的问题,使模式分析更容易应用于其他数据,提出基于迁移学习的交互式时序数据可视化生成方法,将迁移成分分析应用于时序数据提取的特征.将用户在其中一个时序数据上的分析作为标签,在该源域上训练分类器并应用到多个目标域,对多个不同目标域的时序数据的模式进行批量推荐.通过真实的天气数据和轴承信号数据的2个案例分析和专家访谈,验证了利用该方法能够提高时序数据探索的效率,减少数据分布不一致问题带来的影响,体现该方法的有效性和实用性.

基于胶囊网络的异常多分类模型

国网公司日益庞大的服务器集群产生的大量生产运行数据,以及实时分析各类设备、系统产生的海量监控数据成为电力IT运维工作的新挑战。异常检测技术作为智能电网信息运维工作的关键技术,可以有效检测运维故障并及时告警,避免损坏敏感设备。目前一些传统异常检测方法检测的异常种类少且精度低,导致故障发现不及时。为了应对这一挑战,提出了基于胶囊网络的多维时间序列异常多分类模型NNCapsNet。首先,应用无监督算法结合专家知识对电网营销业务应用服务器性能监控数据进行预处理和标注。其次,引入胶囊网络进行分类和异常检测。五折交叉验证的实验结果表明,NNCapsNet在包含15类异常的数据集上实现了91.21%的平均分类准确度。还在包含2万条监控数据的数据集上与4个基准模型进行了对比,NNCapsNet在关键评估指标上均取得了较好的结果。

数字贸易对消费者行为的影响研究

本文选取2022年抽样的淘宝数据进行分析与挖掘,并基于K-means算法对买家进行聚类分析,初步筛选出疑似刷单行为的买家和卖家。在剔除这些用户后,又利用回归分析法分析卖家获得评价、信用评价体系、卖家店铺等级对销量的影响;采用LSTM算法对销量数据的时间序列进行预测;通过Apriori关联规则算法找到买家与卖家和商品之间的关联。其中,在卖家获得评价对销量的影响中,建立奖励函数来描述好评和差评的影响,结果显示奖励函数与销量呈正相关关系。在信用评价体系对销量的影响中,服务和发货对销量的影响较大。卖家店铺等级,则无明显关系。预测的销量数据虽没有较好的结果,但给出了合理的解释。关联结果显示,买家与卖家和商品之间有一定的联系,本研究仅供参考。

基于多突变点与模板匹配的用电设备在线状态监测方法

针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。

基于Time-Causality模型的供热用气量预测分析

目前关于时间序列预测的特征选择一直是研究的热点,但很少有学者分析多时间尺度下不同特征对预测的差异。提出基于Granger关系的Time-Causality预测模型,利用Granger关系进行特征选择,引入时间维度作为输入维度,并利用LSTM模型进行实验,在多时间尺度下分析预测供热用气量的特征。实验结果表明:Time-Causality模型能筛选到更有助于用气量预测的特征;从不同的时间尺度预测,所选取的特征不同;每个特征的预测作用也可能会随时间尺度的变化而变化。这为长期和短期预测提供理论和实践支持。

一种基于深度学习的时序病变数据段分类方法

针对在大规模时序医疗数据的分析中现有检测方法检测精度低、检测速度慢等问题,文中提出了一种基于深度学习的时序病变数据段分类方法。该方法在TSTKS(Ternary Search Trees and modified Kolmogorov-Smirnov)算法和滑动窗口理论的基础上,利用深度学习技术实现了对病变数据段的快速准确分类。文中以利用该方法对病变数据段进行分类的结果作为依据,实现了滑动窗口大小的动态调整。通过对真实癫痫脑电信号(Electroencephalogram,EEG)进行分析,证明了所提病变数据段分类方法和基于该分类方法的滑动窗口动态调整机制具有检测速度快、精度较高等优点,可以为大规模时序数据的快速分析研究提供一种新选择。