基于奇异谱分析-灰狼优化-支持向量回归混合模型的黑河正义峡月径流预测

水文预报是水资源优化配置的重要前提,而传统预报方法普遍存在预测精度低的问题,为提高水文预报的准确性,提出了一种混合数据驱动模型用于月径流预测,即奇异谱分析-灰狼优化-支持向量回归(SSA-GWO-SVR)模型。该模型通过SSA对径流数据进行去噪处理来提高径流序列的平稳性和可预测性,采用GWO对SVR模型的参数进行联合选优,从而增强模型的泛化能力。通过黑河正义峡的月径流预测进行模型验证,以平均绝对误差(MAE)、均方根误差(RMSE)、相关系数(R)和纳什效率系数(NSEC)为模型评价标准。实验结果表明该模型的预测精度明显高于自回归积分滑动平均模型(ARIMA)、持续性模型(PM)、交叉验证-SVR(CV-SVR)和GWOSVR模型,并且它能很好地预测径流峰值,说明该模型是一种可靠的径流预测模型,能够更深入地捕获水文径流的内在特性,为基于数据驱动模型的水文预报提供了一种新方法。     

Monthly Runoff Prediction of Zhengyixia in the Heihe River based on Singular Spectrum Analysis-grey Wolf Optimizer-support Vector Regression Hybrid Model

Hydrologic prediction is an important prerequisite for optimal allocation of water resources, but the traditional forecasting methods generally have the problem of low forecasting accuracy. To improve the accuracy of hydrologic prediction, a hybrid data-driven model is proposed for monthly runoff forecasting, namely, Singular Spectrum Analysis-Grey Wolf Optimizer-Support Vector Regression (SSA-GWO-SVR) model. The proposed model uses SSA to denoise the runoff data to improve the stability and predictability of runoff series, and uses GWO to optimize the parameters of SVR model to enhance the generalization ability of the model. This model is validated by monthly runoff prediction of Zhengyixia in the Heihe River Basin, and the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (R) and Nash-Sutcliffe Efficiency Coefficien (NSEC) are used as evaluation criteria. The experimental results show that the prediction accuracy of the proposed model is significantly higher than those of Autoregressive Integrated Moving Average model (ARIMA), Persistent Model (PM), Cross Validation(CV)-SVR and GWO-SVR models, and the can predict the runoff peak well, which indicates that the model is a reliable runoff forecasting model, can capture the intrinsic characteristics of hydrologic runoff more deeply, and provides a new method for hydrologic prediction based on data-driven model.     

基于贝叶斯模型的云服务服务质量预测

针对如何分配一个未来一段时间内满足QoS要求的云服务和感知可能将要发生的QoS违规的问题,提出一种基于时间序列预测方法的云服务QoS预测方法。该预测方法利用改进的贝叶斯常均值(IBCM)模型,能够准确地预测云服务未来一段时间内的QoS状态。实验通过搭建Hadoop集群模拟云平台并收集了响应时间和吞吐量两种QoS属性的数据作为预测对象,实验结果表明:相比自回归积分滑动平均(ARIMA)模型和贝叶斯常均值折扣模型等时间序列预测方法,基于改进的贝叶斯常均值模型的云服务QoS预测方法的平方和误差(SSE)、平均绝对误差(MAE)、均方误差(MSE)和和平均绝对百分比误差(MAPE)均比前两者小一个数量级,因此具有更高的预测精度;同时预测结果对比图说明提出的预测方法具有更好的拟合效果。     

基于二次分解重构策略的航空客流需求预测

考虑到航空客流需求序列的季节性、非线性和非平稳等特点,提出了一个基于二次分解重构策略的航空客流需求预测模型。首先,通过STL和自适应噪声互补集成经验模态分解（CEEMDAN）方法对航空客流需求序列进行二次分解,并根据数据复杂度和相关度的特征分析结果进行分量重构;然后,采用模型匹配策略分别选取自回归单整移动平均季节（SARIMA）、自回归单整移动平均（ARIMA）、核极限学习机（KELM）和双向长短期记忆（BiLSTM）网络模型对各重构分量进行预测,其中KELM和BiLSTM模型的超参数通过自适应树Parzen估计（ATPE）算法确定;最后,将重构分量预测结果进行线性集成。以北京首都国际机场、深圳宝安国际机场和海口美兰国际机场的航空客流数据作为研究对象进行了1步和多步预测实验,实验结果表明,与一次分解集成模型STL-SAAB相比,所提模型的均方根误差（RMSE）提升了14.98%～60.72%。可见以“分而治之”思想为指导,所提模型结合模型匹配和重构策略挖掘出了数据的内在发展规律,从而为科学预判航空客流需求变化趋势提供了新思路。     

Applications of AR*-GRNN model for financial time series forecasting

AR* models contain Autoregressive Moving Average and Generalized Autoregressive Conditional Heteroscedastic class model which are widely used in time series. Recent researches in forecasting with Generalized Regression Neural Network (GRNN) suggest that GRNN can be a promising alternative to the linear and nonlinear time series models. In this paper, a model composed of AR* and GRNN is proposed to take advantage of their feathers in linear and nonlinear modeling. In the AR*-GRNN model, AR* modeling improves the forecasting performance of the combined model by capturing statistical and volatility information from the time series. The relative experiments testify that the combined model provides an effective way to improve forecasting performance which can be achieved by either of the models used separately.     

Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

The Empirical Mode Decomposition (EMD) has been applied successfully in many forecasting problems. The Variational Mode Decomposition (VMD), a more effective decomposition technique has been proposed with an aim to avoid the limitations of EMD. This study focuses on two objectives i.e. day ahead stock price prediction and daily trend prediction using Robust Kernel based Extreme Learning Machine (RKELM) integrated with VMD where the kernel function parameters optimized with Differential Evolution (DE) algorithm here named as DE-VMD-RKELM. These experiments have been conducted on BSE S&P 500 Index (BSE), Hang Seng Index (HSI) and Financial Times Stock Exchange 100 Index (FTSE), and the daily price prediction performance of the proposed VMD-RKELM model is measured in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). On the other hand the daily trend prediction which is defined as a classification problem is measured in terms of Percentage of Correct Classification Accuracy (PCCA). The prediction performance of the VMD-RKELM is compared with the performance of robust Extreme Learning Machine (RELM), Extreme Learning Machine integrated with EMD (EMD-RELM). Robust Kernel Extreme Learning Machine integrated with EMD (EMD-RKELM) and two benchmark approaches i.e. Support Vector Regression (SVR) and Autoregressive Moving Average (ARMA). The trend prediction results are compared with Naive–Bayes classifier, ANN (artificial neural network), and SVM (support vector machine). The experimental results obtained from this study for price prediction as well as trend classification performance are promising and the prediction analysis illustrated in this work proves the superiority of the VMD-RKELM model over the other predictive methods.     

基于小波分形自回归整合滑动平均模型的网络流量预测

研究表明Internet通信流量同时呈现长相关和短相关特性。为了精确捕捉上述通信流特性,提出一种基于小波分形自回归整合滑动平均(W-FARIMA)模型的预测方法。首先通过Haar小波的方法将原始数据分解为高频信号和低频信号,接着采用FARIMA模型对低频信号进行建模并预测序列,然后对高频信号采用加权一阶局域法进行预测,最后利用小波重构以合成数据。实验和数学分析的方法证实了该预测模型确实能够很好地进行网络流量的长期预测。     

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet, Huber Regressor, Lasso Lars, Passive Aggressive Regressor, K-Neighbors Regressor, Decision Tree Regressor, Extra Trees Regressor, Support Vector Regressions (SVR), AdaBoost Regressor, Random Forest Regressor, Bagging Regressor , AuoRegression, MovingAverage, Gradient Boosting Regressor, Autoregressive Moving Average (ARMA), Auto-Regressive Integrated Moving Averages (ARIMA), SimpleExpSmoothing, Exponential Smoothing, Holt-Winters, Simple Moving Average, Weighted Moving Average, Croston, and naive Bayes. Furthermore, our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods. A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance. Then, this best method is used for future forecasts, and consequently, we can collect the most accurate and dependable predictions.     

软件可靠性预测的ARIMA方法研究

对基于求和自回归滑动平均模型（ARIMA模型）的软件可靠性预测方法进行了研究,提出了将软件可靠性失效数据看作时间序列,通过建立相应的ARIMA（p,d,q）模型来进行预测的方法。对该方法的基本思想、模型表述、建模流程进行了详细介绍,并依据上述方法选用Musa经典数据集中的Project SS2中的数据进行了预测,结果表明预测的准确性较高,说明该方法适用于软件可靠性预测。     

应用乘积季节ARIMA模型的话务量预测及结果分析

话务量预测功能对于电信网络规划建设、网络优化意义重大。深入研究了某省某移动网络运营商的多年的话务量数据,利用自相关函数对其周期性和趋势性方面的规律进行了探测,并在此基础上提出应用乘积季节ARIMA模型进行建模和预测的方案。进行了2008年7月到12月的全省及各地区月日均话务量的预测,并与网络实际运营结果进行了比较。所应用方法的一步预测值平均绝对百分比误差MAPE为1.382%,6步预测的MAPE值均在6%以内,是精确度很高的预测;对预测误差较大的某地区进行了原因分析,证明了模型的正确性,并为实际预测应用中经常遇到的预测误差偏大的问题提供了一种有效的分析思路和方法。     

基于ARIMA与SVM的国际铀资源价格预测

由于国际铀资源价格时间序列数据的非线性性与非平稳性,使用单一的预测模型很难捕捉到其综合趋势。为了进一步提高模型的预测精度,建立了基于差分自回归移动平均（ARIMA）和支持向量机SVM的组合预测模型,并用PSO算法对SVM模型中的参数进行优化。将该方法应用于实际铀资源价格预测,并与单一的ARIMA模型和SVM模型进行比较。仿真实验结果表明,该组合预测模型实现了对铀资源价格数据更为准确的预测。     

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。     

基于双重预测模型的非线性时间序列预测

在支持向量机( SVM)预测问题中,为了减小错误参数选取对预测结果的影响,提出了1种基于双重预测模型的非线性时间序列预测算法.该算法在充分考虑支持向量机参数对推广能力影响的基础上,分别利用自回归预测模型(AR)、自回归滑动平均模型( ARMA)、线性回归和决策树模型对SVM参数进行预测,将预测参数运用到SVM预测模型中...     

基于小波分析的时间序列数据挖掘

将小波分析和ARMA模型引入时间序列数据挖掘中。利用小波消噪对原始时间序列进行滤波,利用小波变换充分提取和分离金融时间序列的各种隐周期和非线性,把小波分解序列的特性和分解数据随尺度倍增而倍减的规律充分用于BP神经网络和自回归移动平均模型的建模。利用小波重构技术将各尺度域的预报结果组合成为时间序列的最终预报。经过试验验证了该方法的实际有效性。     

一种新的统计预测模型——多项式系数自回归模型

传统的自回归滑动平均模型（ARMA）和新近出现的函数系数自回归模型（FAR）不能满足非线性时间序列预测分析的准确度与运算速度要求,为了改进预测性能,研究提出了一种新的统计预测模型——多项式系数自回归模型（PCAR）。给出了PCAR模型的表示形式,详细探讨了PCAR模型的参数估计和阶次选择方法,在此基础上又提出了基于BIC准则的建模算法。同ARMA模型相比,PCAR模型扩大了适用对象范围,有效降低了模型选择误差;同FAR模型相比,它具有参数模型的特点,避免了系数函数局部线性回归估计所存在的不足;分析了PCAR模型与ARMA、FAR模型的等价条件。通过实验分析得出了PCAR模型较ARMA、FAR模型的单步预测准确度分别提高了99.65%和18.7%的结论,而且PCAR建模运算所需时间仅为FAR模型的0.2%。     

基于金融技术指标的用电数据分析

针对已有用电数据分析缺乏有效描述趋势性特征的不足,适应性地将金融领域中十字过滤线(VHF)、异同移动平均线(MACD)等技术指标迁移至用电数据分析中,提出了基于金融技术指标的异动检测算法和负荷预测算法.所提异动检测算法通过统计各指标的统计情况划定阈值,并采用阈值检测捕捉用户异常用电行为.所提负荷预测算法通过提取14项与...     

A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing

Nowadays Network function virtualization (NFV) has drawn immense attention from many cloud providers because of its benefits. NFV enables networks to virtualize node functions such as firewalls, load balancers, and WAN accelerators, conventionally running on dedicated hardware, and instead implements them as virtual software components on standard servers, switches, and storages. In order to provide NFV resources and meet Service Level Agreement (SLA) conditions, minimize energy consumption and utilize physical resources efficiently, resource allocation in the cloud is an essential task. Since network traffic is changing rapidly, an optimized resource allocation strategy should consider resource auto-scaling property for NFV services. In order to scale cloud resources, we should forecast the NFV workload. Existing forecasting methods are providing poor results for highly volatile and fluctuating time series such as cloud workloads. Therefore, we propose a novel hybrid wavelet time series decomposer and GMDH-ELM ensemble method named Wavelet-GMDH-ELM (WGE) for NFV workload forecasting which predicts and ensembles workload in different time-frequency scales. We evaluate the WGE model with three real cloud workload traces to verify its prediction accuracy and compare it with state of the art methods. The results show the proposed method provides better average prediction accuracy. Especially it improves Mean Absolute Percentage Error (MAPE) at least 8% compared to the rival forecasting methods such as support vector regression (SVR) and Long short term memory (LSTM).     

Air pollution forecasting based on attention-based LSTM neural network and ensemble learning

With air pollution having become a global concern, scientists are committed to working on its amelioration. In the field of air pollution prediction, there have been good results in experimental research so far, but few studies have integrated weather forecast information and the properties of air pollution drift. In this work, we propose a novel wind-sensitive attention mechanism with a long short-term memory (LSTM) neural network model to predict the air pollution - PM2.5 concentrations by considering the influence of wind direction and speed on the changes of spatial–temporal PM2.5 concentrations in neighbouring areas. Preliminary predictions for PM2.5 are then made by an LSTM neural network regarding neighbouring pollution; these predictions are “paid attention to” and we finally apply an ensemble learning method based on e X treme G radient B oosting (XGBoost) to combine the preliminary predictions with weather forecasting to make second phase predictions of PM2.5. The experiment is conducted using PM2.5 data and weather forecast data. Our results illustrate that the proposed method is superior to other methods in predicting PM2.5 concentrations, including multi-layer perceptron, support vector regression, LSTM neural network, and extreme gradient boosting algorithm.     

An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models

This article studies monthly volatility forecasting for the copper market, which is of practical interest for various participants such as producers, consumers, governments, and investors.Using data from 1990 to 2016, we propose a framework composed of a set of time series models such as Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), non-parametric models from soft computing, e.g. Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS), and hybrid specifications of both. The adaptability characteristic of these models in exogenous variables, their configuration parameters and window size, simultaneously, are provided by a Genetic Algorithm in pursuit of achieving the best possible forecasts. Also, recognized drivers of this specific market are considered.We examine out-of-sample performance based on Heteroskedasticity-adjusted Mean Squared Error (HMSE), and we test model superiority using the Model Confidence Set (MCS). The results show that making forecasts using an adaptive technique is crucial to obtaining robust and improved performance. The Adaptive-GARCH–FIS specification yielded the best forecasting power.     

二重趋势时间序列的灰色组合预测模型

神经网络、ARIMA等广泛应用于具有趋势变动性和周期波动性的二重趋势特征的时间序列预测,而这些单一的模型难以达到满意的预测效果。提出一种针对该特征的灰色组合模型,其基本思想是：从二重趋势时间序列中分离趋势变动项和周期波动项后,用灰色G（1,1）模型预测趋势变动项,引用BP网络和ARIMA的组合模型预测周期波动项,用乘积模型合成两部分预测值为灰色组合模型的最终预测值。实验表明：该灰色组合模型适应了二重趋势时间序列的特征,具有很好的预测效果。