基于ARIMA和BP神经网络的生态系统服务价值预测仿真

针对现有生态系统服务价值预测体系误差大、研究不足等问题,本文构建了面向未来生态服务价值的预测机制。该机制采用主成分分析法和Person相关系数法,建立了以GDP、人口、土地利用率、土地管理政策和城市人口密度为驱动力的生态系统服务价值关联矩阵,提出了基于ARIMA和BP神经网络的生态系统服务价值的时间序列预测机制。为验证预测机制有效性,本文选取中国主要土地利用类型代表省份的真实土地数据集进行分析,研究结果表明本文建立的预测模型平均绝对误差仅为0.023,且从预测结果来看,未来草地生态会向较好趋势发展,林地生态发展不容乐观。     

基于ARIMA时间序列模型的中国塑料制品产量预测研究

根据中国2000~2019年塑料制品产量的时间序列数据,研究差分自回归移动平均模型ARIMA(p,d,q)的建模和应用。利用白噪声检验和平稳性检验对原始序列进行预处理及ARIMA模型识别,在模型定阶后进行参数估计,检验模型拟合效果,并预测塑料制品的产量。结果表明:ARIMA(2,1,1)模型可以很好地描述塑料制品产量的变化趋势,使用该模型可以预测未来五年的塑料制品产量。     

基于ARIMA的矿山电网故障暂态行波波头辨识及故障测距

由于矿山电网含有大量的整流设备及非线性负载,运行时含有稳定的高次谐波分量和高频噪声,同时矿山电网多为短距离线路,故障后产生的暂态信号与原有高次谐波混叠严重,给行波故障测距带来了极大的困难。通过分析矿山电网故障行波的时域特征,提出基于整合移动平均自回归模型(ARIMA)对行波波头到达前的高频周期信号进行预测,并结合波头到达时刻的真实波形得到波形残差,同时对残差进行平稳性校验,通过行波波头到达时刻前后残差平稳性的不同确定准确的波头到达时刻,进而实现行波故障测距。利用低压电缆网络仿真实现矿山电网故障,仿真结果表明:与小波变换与经验模态分解相比,所提方法能够准确辨识行波波头,且不易受故障状况和噪声的影响,能有效提升行波可行性及精度,尤其适用于含有整流设备及非线性负载矿山电网故障测距。     

Prediction and Analysis of O3 Based on the ARIMA Model

Despite of the small amount in the atmosphere, ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun''s high frequency ultraviolet radiation. In recent decades, the global ozone depletion caused by human activities is well known and produces an "ozone hole", the most direct consequence of which is the increase in ultraviolet radiation, which will affect human survival, climatic environment, ecological environment and other important adverse impacts. Due to the implementation of the Montreal protocol and other agreement, the total amount of ozone depleting substance in the atmosphere has been prominent reduced, which will lead to a new round of regional climate change. Therefore, predicting the changes of the total ozone in the future will have an important guiding significance for predicting the future climate change and making reasonable measures to deal with the climate change. In this paper, based on the ozone data of 1979 to 2016 in the southern hemisphere and ARIMA model algorithm, using time series analysis, we obtain prediction effect of ARIMA model is good by Ljung-Box Q-test and , and the model can be used to predict the future ozone change. With the help of SPSS software, the future trend of the total ozone can be predicted in the future 50 years. Based on the above experiment results, the global ozone change in the future 50 years can be forecasted, namely the atmospheric ozone layer will return to its 1980''s standard by the middle of this century at the global scale.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

基于ARIMA模型的渔业经济预测及其优化

渔业作为国民经济的重要基础之一，对其进行预测十分必要。本文采用时间序列ARIMA模型对渔业总产值进行预测，根据模型预测结果进行误差分析。考虑通货膨胀对预测模型的影响，利用居民消费价格指数（CPI）对模型进行进一步优化。进而以江苏省渔业总产值为例，将1995-2014年的数据作为训练样本，建立模型并结合CPI指数对其优化，以2015-2018年数据作为测试样本，验证了优化模型具有较好的预测效果。     

基于ARIMA的电网地磁感应电流预测研究

地磁感应电流(GIC)对电网安全运行会带来影响,GIC的分析与预测是近来研究的重点,文中应用ARIMA模型对某一时段内的GIC进行建模分析,挖掘其内在的趋势规律,并运用建立的模型进行短期预测.所建立的模型通过了适应性检验和参数的显著性检验,误差在(8％～21％)范围内,能较好的应用于GIC的预测,为保证电网的安全运行提供理论支持.     

基于组合模型高精度预测弹丸径向速度的方法

利用连续波雷达测试弹丸径向速度时,会遇到弹丸、火炮、雷达及外界因素异常,测试的径向速度会出现缺失,导致递推出的炮口初速不准确。为此,选择建立合理的模型预测出缺失的径向速度对数据进行重构。雷达测试的径向速度属于一维数据,大口径弹丸的径向速度主要包含线性特征,小口径弹丸的径向速度既包含线性特征又包含非线性特征,都可以建立ARIMA模型、GM(1,1)灰色模型和回归模型进行预测。但是这些传统模型有时预测能力比较局限,预测精度不理想。为了充分整合所有模型的预测优势,提高预测精度,选择建立组合模型进行预测。针对大口径弹丸,建立由ARIMA、GM(1,1)和一阶线性回归方程构建的组合模型进行预测,针对小口径弹丸,建立由ARIMA、GM(1,1)和二次多项式回归方程构建的组合模型进行预测,为了保证预测精度,按照迭代的方式进行预测。实验结果表明,无论是大口径弹丸还是小口径弹丸,组合模型的预测精度始终高于单项模型,平均相对误差小于1‰,更加适合作为弹丸径向速度的预测模型。     

Book Review

Abraham Boyarsky and Pawel Gora, Laws of Chaos: Invariant Measures and Dynamical Systems in one Dimension, Probability and Its Applications     

Non-stationary functional series modeling and analysis of hardware reliability series: a comparative study using rail vehicle interfailure times

A novel reliability modeling and analysis framework based upon the distinct class of non-stationary Functional Series (FS) models is introduced. This framework allows for non-stationary reliability modeling, evolution assessment, analysis (including non-stationarity assessment, dependency assessment, as well as cycle detection), and prediction. The Functional Series framework is used for the modeling and analysis of two rail vehicle reliability series (Times Between Failures, TBFs), while comparisons with alternative (ARIMA, adaptive RARMA–RML, and Bayesian) modeling approaches are also made. The results indicate the advantages and usefulness of the Functional Series framework, as the TBF modeling accuracy is improved, its non-stationarity and serial dependency are established, the presence of cyclic patterns is revealed, and reliability evolution is assessed. It is conjectured that the cycles revealed in the TBF series may be related to maintenance policies. Finally, reliability prediction is shown to be feasible, although the “larger” excursions in the TBF series are difficult to accurately predict.