高性能存储系统的读请求的建模和预测

为了改善高性能计算系统的I／0性能，本文设计和实现了一个读请求的空间和时间特性的综合预测模型-马尔可夫空间模型与自回归集成的移动平均时间模型，该模型在应用程序执行过程中被自动识别；每个新的请求到达时模型参数被实时递归地估计，并根据应用程序本身和运行环境自适应改变。经过实验检验我们的建模和预测比较有效，测试例子表明时间模型的预测值与观测值的相对误差的平均值为0．16％，并且预测的稳定性较好；空间模型的预测准确性也较好。     

船舶定位信号短时中断下的插值预测模型?

根据基于时间序列的船舶航行定位数据的特征,在差分自回归移动平均模型的基础上,运用马尔可夫链状态转移概率特性解决非平稳数据的预测问题,在建立马尔可夫链状态迁移概率矩阵过程中,使用K-means聚类算法划分预测值与真实值的差值状态区间,继而构建出优化预测算法。对算法进行了理论分析和数值实验,并与其他算法进行了比较,结果表明,该优化算法在船舶定位数据短时预测领域具有较好的预测效果,优于多个其他算法,可应用于船舶移动定位产品中。     

基于ARMA-RESN的网络流量预测

为了获得更加理想的网络流预测结果,融合回声状态网络和自回归移动平均模型的优点,提出一种基于ARMA-RESN的网络流量预测模型。分别采用自回归移动平均和回声状态网络对网络流量线性变化特征和非线性变化特性进行建模与预测,对自回归移动平均和回声状态网络的预测结果进行融合,得到网络流量的最终预测结果,最后采用具体网络流量数据以及多个对比模型进行了仿真实验。仿真结果表明,相对于其他网络流量预测模型,ARMA-RESN不仅提高了网络流量的预测精度,而且具有更好的鲁棒性。     

智慧农业中时序数据组合预测模型

智慧农业是实现农业精准化的技术解决方案,智慧农业系统可以实时监测植物生长的各类环境参数,并可以应用相应的预测模型来模拟农作物生长环境的变化趋势,为科学决策提供依据。近年来有很多学者提出了时间序列的预测模型算法,在预测稳定性方面取得了不错的效果。为了进一步提升时间序列的预测精度,提出一种基于差分整合移动平均自回归模型和小波神经网络的组合预测模型。该组合模型结合2个单项模型优点,用差分整合移动平均自回归模型来拟合序列的线性部分,用小波神经网络来校正其残差,使其拟合曲线更接近于实际值,采用温室内的历史温度数据来验证该组合模型的精确度,最后将组合模型与传统预测模型的预测结果进行对比。结果表明,该组合模型用于温室温度预测的精确度更高,拟合效果更好,相比于传统模型预测算法计算效能提高了20%左右。     

应用DRNN和ARIMA组合模型的时空集成预测方法

在评析目前时空预测研究现状的基础上,提出基于动态回归神经网络(DRNN)和自回归集成移动平均(ARIMA)组合模型的时空集成预测方法.该方法先用ARIMA模型对时空数据的时序进行预测,再用DRNN捕获时空数据间隐藏的空间关系,最后用线性回归将二者整合起来,得到集成预测结果.案例实验结果表明:该方法比不考虑空间影响的预测方法或单一的预测方法有更高的精度;该方法具有良好的动态处理和计算能力,对跨空间的动态过程的预测有效可行.     

基于DEMETER卫星观测数据的电离层离子温度时间序列预测模型

提出了一种利用DEMETER卫星观测量构建电离层离子温度背景场及预测模型的时间序列分析方法。首先,通过分析卫星轨道重访周期与建模格网大小的关系,确定了时间序列采样轨道数据的空间区域大小为1.75°×1.75°、时间间隔约为14天。然后,基于电离层离子温度明显的季节变化特征,采用自回归移动平均模型(ARIMA)构建时间序列预测模型来描述电离层离子温度及其周期性变化。最后,采用2006年～2009年的DE-METER卫星观测数据验证了该时间序列预测模型,结果表明ARIMA季节模型能较好地模拟电离层离子温度在时间上的变化趋势,建立较为可靠的电离层背景场。     

优选时间序列数据模型的人工智能算法

为了解决大量文献对时间序列数据的建模、仿真及预测的研究存在不做模型选择,直接应用某种模型进行分析的局限,针对经常应用于时间序列分析的3种人工智能模型:隐马尔可夫模型、人工神经网络模型和自回归移动平均模型进行基于仿真比对方法的模型选择研究。简述3种模型的基本原理,对各类模型进行数值仿真,考察各类模型生成时间序列的特征,以该特征为依据,提出模型选择的理论与算法,应用本文的模型选择理论和算法,进行实证分析。实验结果表明,各类模型生成的时间序列数据有不同的数理特征,其提供了模型选择的依据,同时,该选择理论是实用的,应用该理论选择的模型有较好的拟合和预测效果,为时间序列类型的数据挖掘提供了依据。     

基于指数平滑的云服务器请求量集成预测模型

针对现有弹性云服务器(elastic cloud server,ECS)未来请求量预测模型准确度低、稳定性差等问题,提出一种基于指数平滑的Stacking集成预测模型。以多个二次指数平滑模型作为基础模型,将线性回归模型作为集成模型对多组指数平滑预测值进行最终拟合;预测过程中使用多组二次指数平滑模型对ECS的历史请求时序进行构造集成模型训练数据集并加入平滑系数的动态优化。与传统单一模型的对比实验结果表明,该模型在实际云服务器请求量预测过程中具有更好的准确性和稳定性。     

行程时间预测方法研究

行程时间预测,有助于实施高级旅行者信息系统.自20世纪90年代起,已经有多种行程时间预测方法被研发出来.将行程时间预测方法分为模型驱动方法和数据驱动方法两大类.介绍了两种常见的模型驱动方法,即排队论模型和细胞传输模型.数据驱动方法被分类为参数方法和非参数方法：参数方法包括线性回归、自回归集成移动平均和卡尔曼滤波,非参数方法包括神经网络、支持向量回归、最近邻和集成学习方法.对现有行程时间预测方法从源数据、预测范围、准确率、优缺点和适用范围等方面进行了分析总结.针对现有方法的一些缺点,提出了可能的解决方案.给出了一种新颖的数据预处理框架和一个行程时间预测模型,最后指出了未来的研究方向.     

犯罪量动态优化组合预测方法

单一预测模型在预测犯罪量时难以协调拟合和泛化关系,从而影响预测结果的准确性。针对以上问题,提出一种数据驱动的可动态优化组合预测方法。以分析自回归求和移动平均模型、向量自回归模型及支持向量机模型的优点为基础,使用后验概率为每个模型赋予权重,结合误差最小原则动态调整权重。实验结果表明,该方法具有较高的预测精度和稳定性,能满足短时犯罪量预测的需要。     

Automatic ARIMA time series modeling for adaptive I/O prefetching

Inadequate I/O performance remains a major challenge in using high-end computing systems effectively. To address this problem, we present TsModeler, an automatic time series modeling and prediction framework for adaptive I/O prefetching that uses ARIMA time series models to predict the temporal patterns of I/O requests. These online pattern analysis techniques and cutoff indicators for autocorrelation patterns enable multistep online predictions suitable for multiblock prefetching. This work also combines time series predictions with spatial Markov model predictions to determine when, what, and how many blocks to prefetch. Experimental results show reductions in execution time compared to the standard Linux file system across various hardware configurations.     

基于ARIMA和LSTM混合模型的时间序列预测

由于现实中的时间序列通常同时具有线性和非线性特征,传统ARIMA模型在时间序列建模中常表现出一定局限性.对此,提出基于ARIMA和LSTM混合模型进行时间序列预测.应用线性ARIMA模型进行时间序列预测,用支持向量回归(SVR)模型对误差序列进行预测,采用深度LSTM模型对ARIMA模型和SVR模型的预测结果组合,并将...     

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA–ANN model for the prediction of time series data. Many of the hybrid ARIMA–ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA–ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA–ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.     

A hybrid neural network and ARIMA model for water quality time series prediction

Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 108-month observations of water quality data, including water temperature, boron and dissolved oxygen, during 1996–2004 at Büyük Menderes river, Turkey. Specifically, the results from the hybrid model provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The correlation coefficients between the hybrid model predicted values and observed data for boron, dissolved oxygen and water temperature are 0.902, 0.893, and 0.909, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures. Owing to its ability in recognizing time series patterns and nonlinear characteristics, the hybrid model provides much better accuracy over the ARIMA and neural network models for water quality predictions.     

基于ARIMA-GM的短期民航货邮周转量研究

ARIMA模型对季节特征有较好的拟合效果,灰色GM（1,1）模型能准确反映时间序列的增长趋势,结合民航货邮周转量的特点和ARIMA模型和GM（1,1）模型的优点,分别建立货邮周转量的ARIMA和GM（1,1）的时间序列模型,揭示出民航货邮周转量随时间推移而发展变化的动态规律,最后为更精确地预测月度民航货邮周转量,提出基于ARIMA-GM的组合预测模型,并对近几月民航货邮周转量进行较准确的短期预测,结果表明：组合模型能提高预测精度,在实际应用中ARIMA模型可用于非季节和季节的各类时间序列;灰色GM（1,1）模型能准确反映时间序列的增长趋势,两者相结合很好地解决了民航货邮周转量短期预测的实际问题,得到民航货邮周转量更精确的预测结论,能够对民航货邮市场的发展趋势进行宏观把握,有利于决策者的经济决策行为。     

统计特征和Markov模型在三维模型分类中的应用

针对三维模型的分类问题,提出了一种基于统计特征量和Markov模型的分类算法。该算法对预处理后的三维模型进行几何切分,并提取切分后每块的统计特征。对三维模型各分块进行一定顺序的观测,可以获得由各分块的统计特征量构成的Markov模型的伪时间序列。再对不同类模型进行训练并得到各类模型对应的Markov模型参数。最后定义模型间的相似度度量,获得三维模型的分类结果。实验表明该算法在绝大多数类别的模型上分类效果较好,准确率达到90%以上。     

A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export

This study compares the application of two forecasting methods on the amount of Taiwan export, the ARIMA time series method and the fuzzy time series method. Models discussed for the fuzzy time series method include the Factor models, the Heuristic models, and the Markov model. When the sample period is prolong in our models, the ARIMA model shows smaller than predicted error and closer predicted trajectory to the realistic trend than those of the fuzzy model, resulted in more accurate forecasts of the export amount in the ARIMA model. Especially, the coefficient of the error term for the previous period has increased to 79%, implying the influential effect of external factors. These external factors attribute to the export amount of Taiwan according to the economic viewpoints. However, this impact reduces as time progressing and the export amount of the lag period of 12 or 13 do not affect current export amount anymore. In conclusion, when the sample period is shorter with only a small set of data available, the fuzzy time series models can be utilized to predict export values accurately, outperforming the ARIMA model.     

基于EMD-GAELM-ARIMA算法的大坝变形预测

In view of the fact that it is difficult for statistical models to make good predictions of nonlinear and non-stationary dam deformation, artificial intelligence algorithms are induced. The empirical mode decomposition method (EMD), genetic algorithm (GA) optimized extreme learning machine (ELM), and ARIMA error correction model were used to construct a dam deformation prediction model. First this paper uses EMD to decompose and reconstruct the monitoring data to stabilize it and obtain eigenmode functions and residual sequences with physical significance; then uses GAELM to analyze and predict the decomposition results; finally, uses ARIMA model to correct errors. Taking a concrete rockfill dam as an example, the dam deformation prediction model constructed by the optimization algorithm is used to analyze and predict it. The analysis results show that the EMD-GAELM-ARIMA model algorithm has higher prediction accuracy than the traditional single algorithm. It is feasible in dam deformation prediction.