Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Condition for Successful Square Transformation in Time Series Modeling

In this study we establish the probability density function of the square transformed left-truncated N(1,σ2) error component of the multiplicative time series model and the functional expressions for its mean and variance. Furthermore the mean and variance of the square transformed left-truncated N(1,σ2) error component and those of the untransformed component were compared for the purpose of establishing the interval for σ where the properties of the two distributions are approximately the same in terms of equality of means and normality. From the results of the study, it was established that the two distributions are normally distributed and have means ≌1.0 correct to 1 dp in the interval 0 σ , hence a successful square transformation where necessary is achieved for values of σ such that 0 σ .

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

Local Influence on the Error-Correction Variable in a Cointegrated System

The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.

A Spatiotemporal Interactive Processing Bias Correction Method for Operational Ocean Wave Forecasts

Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictions of numerical models and the actual sea state has been observed,predictions can only be released after correction by forecasters.This paper proposes a spati-otemporal interactive processing bias correction method to correct numerical prediction fields applied to the production and release of operational ocean wave forecasting products.The proposed method combines the advantages of numerical models and Forecast Discussion;specifically,it integrates subjective and objective information to achieve interactive spatiotemporal correc-tions for numerical prediction.The method corrects the single-time numerical prediction field in space by spatial interpolation and sub-zone numerical analyses using numerical model grid data in combination with real-time observations and the artificial judg-ment of forecasters to achieve numerical prediction accuracy.The difference between the original numerical prediction field and the spatial correction field is interpolated to an adjacent time series by successive correction analysis,thereby achieving highly efficient correction for multi-time forecasting fields.In this paper,the significant wave height forecasts from the European Centre for Medium-Range Weather Forecasts are used as background field for forecasting correction and analysis.Results indicate that the proposed method has good application potential for the bias correction of numerical predictions under different sea states.The method takes into account spatial correlations for the numerical prediction field and the time series development of the numerical model to correct numerical predictions efficiently.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

A Reanalysis of the Two Swimmers Problem, as Frequent Model of Michelson’s Interferometric Experiment Demonstrating that Transversal Path Is Not an Isosceles but a Right Triangle and the Race Will End in a Tie

The article initially reviews various works describing the physical model (PM) of Michelson’s interferometric experiment (ME), represented by the race between two swimmers Sw1, Sw2 (or boats, or planes, or sound signals, etc.). The two swimmers must each swim the same distance, but Sw1 will swim along the river flow, and Sw2 will swim perpendicularly to this direction. In all such works, it is considered that Sw2’s path will require less time and that it will reach the start point first. However, in this work, it has been determined that in order to make this possible, Sw2 must not observe the orthogonality rule of his start direction. This action would be deceitful to the arbiters and thus considered as non-fair-play towards Sw1. The article proves by swimming times calculus, that if the fair-play rules are observed, then the correct crosswise path (in water reference frame) is a right triangle instead of the isosceles triangle considered by Michelson. Consequently, the two times shall be perfectly equal and the race ends in a tie, and the myth of Sw2 as the race winner shall be debunked. Note that the same result shall also be applicable to Michelson’s interferometric experiment (ME) as well as to any similar experiment. Therefore, utilising the isosceles triangle as the transversal path in PM and also in ME is an erroneous act.

Volatility Prediction via Hybrid LSTM Models with GARCH Type Parameters

Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment risk,which has always been the focus of attention,is measured by the variance of residual sequence obtained following model regression.By integrating the long short-term memory(LSTM)model with multiple generalized autoregressive conditional heteroscedasticity(GARCH)models,a new hybrid LSTM model is used to predict stock price volatility.In this paper,three GARCH models are used,and the model that can best fit the data is determined.

灰色Verhulst-时间序列组合模型在沉降监测中的应用

针对传统单一的灰色模型、时间序列模型在进行沉降监测数据模型存在精度较低的问题,本文提出了一种灰色Verhulst模型和时间序列模型的组合优化模型。首先,对灰色Verhulst模型、时间序列模型的基本理论进行阐述;其次,建立了灰色Verhulst-时间序列模型,即以灰色Verhulst模型得到的样本数据拟合值作为时间序列模型进行预测的样本值;最后,通过以某建筑物沉降监测点为例进行计算并进行灰色Verhulst模型、时间序列模型以及灰色Verhulst-时间序列模型的预测结果对比。实验结果表明,灰色Verhulst-时间序列组合优化模型的预测精度最高,均方差为0.3275 mm,验证了该组合模型更贴合建筑物沉降的变形趋势。

改进Verhulst模型在饱和负荷预测中的应用

为实现对宏观负荷的饱和时间点、饱和规模的预测,提出了一种基于改进Verhulst模型的饱和负荷预测方法。针对饱和负荷预测时间跨度长、负荷规模增长呈现"S"型的特点,将等维新息递补技术引入灰色Verhulst模型,使预测结果能够更科学合理地反映用电需求的发展规律。采用残差修正的思想,构造Verhulst残差修正模型,实现对用电需求的分析与预测。最后,通过实例分析说明该方法的有效性。

边坡非线性位移的Verhulst-ARMA组合预测模型研究

针对ARMA模型中时序变量自身变化的复杂性及其预测的不确定性,利用Verhulst提取非平稳时间序列中的趋势项,再对剩下部分采用平稳时间序列建模,建立Verhulst-ARMA组合预测模型。运用文中组合模型对三峡某边坡滑移的实测数据进行分析,验证Verhulst-ARMA组合模型在边坡非线性位移预测中的可靠性和适用性。

港口吞吐量预测的时序残差修正Verhulst模型

灰色系统预测模型是一种进行港口吞吐量预测的有效方法。但是,当港口吞吐量按照“S”型曲线增长或增长处于饱和阶段时,采用灰色模型进行吞吐量预测的误差较大,预测精度不能满足实际要求。根据港口吞吐量的增长规律,通过典型实例提出了基于时序残差的港口吞吐量预测Verhulst模型,用于中长期港口吞吐量预测。应用结果表明,本模型对于那些暂时处于快速增长而从长远看按“S”型曲线增长的港口吞吐量预测具有较高的预测精度,同时保留了灰色预测方法的原有优势和特点。

灰色Verhulst模型在预压法处理地基中的应用研究

基于生物学Verhulst模型,以原始累积沉降量时间序列为基础建立等间隔Verhulst地基沉降预测模型;并在此模型基础上,引入加权平均弱化缓冲算子(WAWBO),采用动态定权方法,建立非等间隔灰色Verhulst模型。借助珠海横琴能源站项目地基沉降监测数据,分别检验等间隔和非等间隔Verhulst模型的可靠性,并对两种模型预测结果与三点法,双曲线法,Asaoka法预测结果进行对比分析;研究结果表明:两种Verhulst模型分别满足"合格"和"好"的精度等级要求;与常用的三点法,Asaoka法,双曲线法计算结果相比,非等间隔灰色Verhulst模型计算值更接近真实值。

基于Verhulst与线性回归组合模型的港口吞吐量预测研究

由于港口吞吐量预测的复杂性,在许多情况下,单纯利用一种特定的预测方法进行预测往往具有片面性,为尽可能使预测结果具有较高可信度,基于"误差平方和最小"为最优准则,建立灰色Verhulst时序残差修正模型与一元线性回归模型的组合模型,将其用于某港口货物吞吐量预测,经验证组合模型具有较好的预测效果,并对该港口未来3年的货物吞吐量进行了预测。

基于多源信息融合的船舶电气设备状态识别方法

为可靠掌握船舶电气设备状态,保证设备的运行安全,提出多源信息融合的船舶电气设备状态识别方法。采用时间序列模型检测并修正船舶电气设备多源历史数据中的连续异常数据和独立异常数据;基于联合卡尔曼滤波算法融合修正后的电气设备多源历史数据,依据融合后的多源数据训练谱聚类和深度神经网络,构建船舶电气设备状态识别网络模型,结合电气设备的实时运行数据,识别船舶电气设备状态。测试结果显示,该方法能够确定数据中的连续异常数据和独立异常数据,并且完成所有异常数据的修正,保证数据的完整性;离散度结果均在0.016以下;能够完成电气设备正常状态、异常状态以及紧急状态的识别,最小均方根误差值均在0.0044以下,识别效果良好。

贵州喀斯特地貌地区GNSS站坐标时间序列特性研究

使用GAMIT/GLOBK10.71软件解算贵州省北斗卫星导航定位基准站网(GZCORS)25个基准站数据,获得各站单天解坐标时间序列。利用主成分分析法提取站坐标共模误差,利用谱分析法分析共模误差基本特性,采用极大似然估计法确定站点最优噪声模型及其运动速度。结果表明,GZCORS坐标序列共模误差中包含有周期项,N方向最大振幅周期分别出现在0.2周/a、1.2周/a、3.2周/a和4.2周/a;GZCORS站点最优噪声模型以WN+FN和WN+GM为主,剔除共模误差后,36%的测站分量噪声特性发生变化;剔除共模误差后,坐标时间序列噪声水平明显降低,各坐标分量速度参数的估计精度均有明显提升,其中N、E、U分量分别提高52%、56%和50%。

基于SVM-STL-LSTM的区域短期电力负荷预测研究

针对区域电力负荷的时间序列数据随机性强、预测精度低及单一模型的数据特征提取能力差等问题,提出了一种支持向量机(SVM)、STL时序分解法、长短期记忆神经网络(LSTM)组合的电力负荷预测模型。该模型利用SVM对时间序列的电力负荷数据进行初始预测,并通过STL时序分解法对残差序列进行时序分解,从而提高残差序列的稳定性,减小其随机性,最后用LSTM对SVM的预测误差进行修正。试验结果证明,该方法利用误差修正可有效处理随机性强的数据,有利于预测结果的稳定性,提高预测精度。

川藏地区CORS坐标时间序列预处理与软件实现

本文针对川藏地区连续运行参考站系统(continuously operating reference stations,CORS)基准站站点坐标包含复杂影响因子和丰富且微弱有益的信号的特点,为了解决区域大规模时间序列数据分析处理过程繁琐,且无法大批量处理的问题,设计开发了GNSS坐标时间序列预处理系统,支持大区域多站点批处理解算模式,实现数据产品预处理、下载和可视化,实现了最小二乘拟合、粗差剔除、建模插值和共模误差(common mode error,CME)改正等一体化功能模块的集成.采用陆态网长期坐标时间序列数据从解算精度与效率两方面对软件进行了性能评估.结果表明:陆态网各测站N方向和E方向最小二乘拟合的拟合优度R2均约在99%,拟合效果较好;粗差剔除后的时间序列相比于剔除前各方向的WRMS值都有降低;水平方向插值后的结果均方根误差(root mean square error,RMSE)值均优于8 mm.CME剔除后,水平方向和垂直方向均方根(root mean square,RMS)值均有所下降.

具有一阶自回归误差的半参数可加模型的估计

研究具有一阶自回归误差的半参数可加时间序列模型的估计问题.假设回归函数来源于某个参数分布族,采用局部二次拟合准则,结合非参数核函数方法,给出参数向量的估计量和回归函数的半参数估计量.在一定正则条件下,证明估计量具有相合性.通过模拟研究与实证分析检验方法的有效性和可行性.

2011-2021年浙江省肺结核发病率预测:基于三体模型和三体预测法

目的研究三体模型和三体预测法在预测肺结核发病趋势中的应用。方法使用浙江省2011—2021年肺结核月度发病率数据,基于三体模型和三体预测法构建预测模型,并评估该预测模型的预测性能。结果基于三体模型和三体预测法获得的预测模型1和预测模型2的平均相对预测误差分别为7.94%、8.43%,而使用自回归移动平均(ARIMA)模型获得的平均相对预测误差为8.87%,以上平均相对预测误差均处于区间(7.9%~8.9%),显示预测模型表现优秀。结论三体模型是表现优秀的时间序列预测模型,三体预测法是表现优秀的时间序列预测方法,具有较高的应用价值。