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...     

融合结构化信息与时序演化信息的多变量间歇性时间序列预测

复杂装备制造企业的售后配件需求不定时发生、需求波动大,导致需求数据呈现典型的间歇性、小样本特点.当面对间歇性程度高、突发需求较大的序列时,现有预测模型难以准确捕获其需求波动规律,无法有效预测配件需求走势.为提高多组配件的预测精度和稳定性,同时考虑序列间结构化信息和序列自身时序演化信息,提出一种新的多变量间歇性时间序列预测方法.首先,提出一种基于张量的轻型梯度提升机模型,通过张量分解,重构原始需求数据,修正序列中的异常需求值,并利用轻型梯度提升机对多组序列进行联合预测;然后,构建一种新的线性衰减修正模型,将修正因子引入线性衰减指数平滑方法,对每条序列分别预测需求量和间隔区间;最后,将2个预测模型进行加权融合,得到最终预测结果.分别在2个复杂装备制造企业的售后配件需求数据集上进行实验验证,实验结果表明,与多个时间序列预测算法相比,所提出方法能够有效预测需求波动趋势,提升预测精度和数值稳定性.     

A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction

Air quality is closely related to concentrations of gaseous pollutants, and the prediction of gaseous pollutant concentration plays a decisive role in regulating plant and vehicle emissions. Due to the non-linear and chaotic characteristics of the gas concentration series, traditional models may not easily capture the complex time series pattern. In this study, the Gaussian Process Mixture (GPM) model, which adopts hidden variables posterior hard-cut (HC) iterative learning algorithm, is first applied to the prediction of gaseous pollutant concentration in order to improve prediction performance. This algorithm adopts iterative learning and uses the maximizing a posteriori (MAP) estimation to achieve the optimal grouping of samples which effectively improves the expectation–maximization (EM) learning in GPM. The empirical results of the GPM model reveals improved prediction accuracy in gaseous pollutant concentration prediction, as compared with the kernel regression (K-R), minimax probability machine regression (MPMR), linear regression (L-R) and Gaussian Processes (GP) models. Furthermore, GPM with various learning algorithms, namely the HC algorithm, Leave-one-out Cross Validation (LOOCV), and variational algorithms, respectively, are also examined in this study. The results also show that the GPM with HC learning achieves superior performance compared with other learning algorithms.     

Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study

The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm. In this study, several neural and neurofuzzy models with different learning algorithms are examined for prediction of several benchmark chaotic systems and time series. The prediction performance of locally linear neurofuzzy models with recently developed Locally Linear Model Tree (LoLiMoT) learning algorithm is compared with that of Radial Basis Function (RBF) neural network with Orthogonal Least Squares (OLS) learning algorithm, MultiLayer Perceptron neural network with error back-propagation learning algorithm, and Adaptive Network based Fuzzy Inference System. Particularly, cross validation techniques based on the evaluation of error indices on multiple validation sets is utilized to optimize the number of neurons and to prevent over fitting in the incremental learning algorithms. To make a fair comparison between neural and neurofuzzy models, they are compared at their best structure based on their prediction accuracy, generalization, and computational complexity. The experiments are basically designed to analyze the generalization capability and accuracy of the learning techniques when dealing with limited number of training samples from deterministic chaotic time series, but the effect of noise on the performance of the techniques is also considered. Various chaotic systems and time series including Lorenz system, Mackey-Glass chaotic equation, Henon map, AE geomagnetic activity index, and sunspot numbers are examined as case studies. The obtained results indicate the superior performance of incremental learning algorithms and their respective networks, such as, OLS for RBF network and LoLiMoT for locally linear neurofuzzy model.     

Applying a general regression neural network for predicting development effort of short-scale programs

Software development effort prediction is considered in several international software processes as the Capability Maturity Model-Integrated (CMMi), by ISO-15504 as well as by ISO/IEC 12207. In this paper, data of two kinds of lines of code gathered from programs developed with practices based on the Personal Software Process (PSP) were used as independent variables in three models for estimating and predicting the development effort. Samples of 163 and 80 programs were used for verifying and validating, respectively, the models. The prediction accuracy comparison among a multiple linear regression, a general regression neural network, and a fuzzy logic model was made using as criteria the magnitude of error relative to the estimate (MER) and mean square error (MSE). Results accepted the following hypothesis: effort prediction accuracy of a general regression neural network is statistically equal than those obtained by a fuzzy logic model as well as by a multiple linear regression, when new and change code and reused code obtained from short-scale programs developed with personal practices are used as independent variables.     

Random forests-based extreme learning machine ensemble for multi-regime time series prediction

Accurate and timely predicting values of performance parameters are currently strongly needed for important complex equipment in engineering. In time series prediction, two problems are urgent to be solved. One problem is how to achieve the accuracy, stability and efficiency together, and the other is how to handle time series with multiple regimes. To solve these two problems, random forests-based extreme learning machine ensemble model and a novel multi-regime approach are proposed respectively, and these two approaches can be integrated to achieve better performance. First, the extreme learning machine (ELM) is used in the proposed model because of its efficiency. Then the regularized ELM and ensemble learning strategy are used to improve generalization performance and prediction accuracy. The bootstrap sampling technique is used to generate training sample sets for multiple base-level ELM models, and then the random forests (RF) model is used as the combiner to aggregate these ELM models to achieve more accurate and stable performance. Next, based on the specific properties of turbofan engine time series, a multi-regime approach is proposed to handle it. Regimes are first separated, then the proposed RF-based ELM ensemble model is used to learn models of all regimes, individually, and last, all the learned regime models are aggregated to predict performance parameter at the future timestamp. The proposed RF-based ELM ensemble model and multi-regime approaches are evaluated by using NN3 time series and NASA turbofan engine time series, and then the proposed model is applied to the exhaust gas temperature prediction of CFM engine. The results demonstrate that the proposed RF-based ELM ensemble model and multi-regime approach can be accurate, stable and efficient in predicting multi-regime time series, and it can be robust against overfitting.     

结合灰色扩充的GA-BP神经网络模型在渭河水质遥感反演中的应用

以渭河陕西段水域为研究对象,用遗传算法改进的BP神经网络,结合灰色理论,建立了一种结合灰色扩充的GA-BP神经网络模型,对渭河水质中的主要污染指标CODmn(高锰酸盐指数)、COD(化学需氧量)、NH₃-N(氨氮)、DO(溶解氧)进行了遥感反演建模。实验证明：改进后的人工神经网络模型在预测精度上高于普通的BP神经网络模型和传统的多元线性回归模型,可用于渭河水质遥感反演建模。
     

基于交互式逐步回归的双语教学态度预测模型

针对多元线性回归分析法预测双语教学态度误差较大的问题,通过引入变量的平方项及交叉乘积项建立拟线性回归方程,利用交互式逐步回归分析法对变量进行筛选,并以筛选后的变量建模。将调查数据分为建模样本数据和测试样本数据,测试结果表明经过变量扩维及筛选所建立的预测模型精确度有较大提高,为语言学研究提供了一种新方法,值得推扩和借鉴。     

Sensitivity analysis for predicting continuous fields of tree-cover and fractional land-cover distributions in cloud-prone areas

The use of multi-temporal datasets, such as vegetation index time series or phenological metrics, for improved classification and regression performance is well established in the remote-sensing science community. However, the usefulness of such information is less apparent for areas with distinct wet season periods and heavily concentrated cloud cover. In view of this, this study examines the potential of multi-temporal datasets for the estimation of sub-pixel land-cover fractions and percentage tree cover in an area having distinct wet and dry seasons. Prediction is based on a regression tree algorithm in combination with linear least-squares regression planes, which relate multi-spectral and multi-temporal satellite data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor to sub-pixel land-cover proportions and percentage tree cover, derived from high-resolution land-cover maps. Furthermore, several versions of the latter were produced using different classification approaches to evaluate the sensitivity of the response variable on overall prediction accuracy. The results were evaluated according to absolute accuracy levels and according to their long-term inter-annual robustness by applying the regression models to MODIS data over a period of 11 years. The best regression model based on dry season information only estimated continuous fields of percentage tree cover with a prediction error of less than 7% and an inter-annual variability of less than 4% over a time period of 11 years. The inclusion of intra-annual information did not contribute to any improvements in model accuracy compared to information from the dry season alone, and furthermore, deteriorated inter-annual robustness of model predictions. In addition, it has been shown that the quality of the response variable in the training data had significant effects on overall accuracy.     

多函数激活的拉普拉斯深度回声状态网络

结合可变激活函数、降维算法和深度回声状态网络针对新的神经网络模型进行了研究。其中可变激活函数是多函数的线性组合,可以通过调整系数来改变激活函数的非饱和区;拉普拉斯特征映射降维算法通过降低储层状态矩阵的维度来改善原网络面临的病态、不适定问题;还使用了遗传算法来寻找最佳目标子空间维度。仿真分析从扰动影响、转换稳定性、时序预测和记忆容量四个方面进行,从仿真结果（新模型的记忆容量是深度回声状态网络的两倍,均方根误差比回声状态网络小42%）来看,新模型的记忆容量、预测精度都得到了显著改善。     

遗传算法优化延迟时间和嵌入维的网络流量预测

为了提高网络流量的预测精度,利用延迟时间（τ）和嵌入维（m）间的联系,提出一种遗传算法优化τ、m的网络流量预测模型（GA-PSR）。将τ和m作为遗传算法的个体,以网络流量预测精度作为目标函数,通过选择、交叉、变异等操作找到最优τ和m值,重构网络流量序列,采用BP网络对网络流量建立单步、多步预测模型。仿真实验结果表明,相对于对比模型,GA-PSR提高了网络流量的预测精度。     

A fuzzy-neural knowledge-based system for job completion time prediction and internal due date assignment in a wafer fabrication plant

To further enhance the performance of job completion time prediction and internal due date assignment in a wafer fab, a fuzzy-neural knowledge-based system is constructed in this study. In the constructed system, multiple experts construct their own fuzzy multiple linear regression models from various viewpoints to predict the completion/cycle time of a job. Each fuzzy multiple linear regression model can be converted into an equivalent non-linear programming problem to be solved. Subsequently, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy completion time forecasts into a polygon-shaped fuzzy number, in order to improve the precision of completion time forecasting. The polygon-shaped fuzzy number contains the actual value, and its upper bound determines the internal due date of the job. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. A practical example is used to evaluate the effectiveness of the proposed methodology. According to experimental results, the proposed methodology improved both the precision and accuracy of job cycle time prediction by 16 and 21%, respectively.     

Development of Box–Jenkins type time series models by combining conventional and orthonormal basis filter approaches

A unified scheme for developing Box–Jenkins (BJ) type models from input–output plant data by combining orthonormal basis filter (OBF) model and conventional time series models, and the procedure for the corresponding multi-step-ahead prediction are presented. The models have a deterministic part that has an OBF structure and an explicit stochastic part which has either an AR or an ARMA structure. The proposed models combine all the advantages of an OBF model over conventional linear models together with an explicit noise model. The parameters of the OBF–AR model are easily estimated by linear least square method. The OBF–ARMA model structure leads to a pseudo-linear regression where the parameters can be easily estimated using either a two-step linear least square method or an extended least square method. Models for MIMO systems are easily developed using multiple MISO models. The advantages of the proposed models over BJ models are: parameters can be easily and accurately determined without involving nonlinear optimization; a prior knowledge of time delays is not required; and the identification and prediction schemes can be easily extended to MIMO systems. The proposed methods are illustrated with two SISO simulation case studies and one MIMO, real plant pilot-scale distillation column.     

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.     

基于改进狼群算法的模糊时间序列预测模型

当使用模糊时间序列预测模型进行预测时, 模糊区间的不同划分对最后的预测精度有着十分重要的影响. 针对如何更有效的划分模糊区间、进一步提高模糊时间序列的预测精度问题, 本文提出了一种基于改进狼群算法 的模糊时间序列预测模型. 为此首先简要介绍了模糊时间序列, 然后阐述了狼群算法并在其游走行为中引入趋向 行为和死亡概率对其进行了改进, 最后利用改进狼群算法来划分模糊区间, 建立了一种新的模糊时间序列预测模 型. 将Alabama大学入学人数作为实验数据进行实例分析和验证. 通过与现有的一些模型进行对比分析, 本文所提 模型具有更高的预测精度, 为模糊时间序列预测提供了新思路.     

BP网络和多元回归在葡萄酒质量模型中的应用

利用因子分析法筛选出对葡萄酒质量影响较大的12种理化指标,将其作为多元线性回归的自变量和BP网络输入层神经元,分别用多元线性回归和改进的BP神经网络两种方法建立葡萄酒和酿酒葡萄的主要理化指标与葡萄酒质量的关系模型。比较了两种模型的泛化能力,得出多元线性回归模型对新样本预测的平均相对误差是1.93%,而BP神经网络模型的平均相对误差是0.37%。仿真实验表明,BP神经网络的泛化能力和稳定性明显优于多元回归模型。     

Short-term freeway traffic parameter prediction: Application of grey system theory models

Intelligent transportation systems applications require accurate and robust prediction of traffic parameters such as speed, travel time, and flow. However, traffic exhibits sudden shifts due to various factors such as weather, accidents, driving characteristics, and demand surges, which adversely affect the performance of the prediction models. This paper studies possible applications and accuracy levels of three Grey System theory models for short-term traffic speed and travel time predictions: first order single variable Grey model (GM(1,1)), GM(1,1) with Fourier error corrections (EFGM), and the Grey Verhulst model with Fourier error corrections (EFGVM). Grey models are tested on datasets from California and Virginia. They are compared to nonlinear time series models. Grey models are found to be simple, adaptive, able to deal better with abrupt parameter changes, and not requiring many data points for prediction updates. Based on the sample data used, Grey models consistently demonstrate lower prediction errors over all the time series improving the accuracy on average about 50% in Root Mean Squared Errors and Mean Absolute Percent Errors.     

基于时间序列的支持向量机在股票预测中的应用

由于股票预测是不确定、非线性、非平稳的时间序列问题,传统的方法往往难以取得满意的预测效果。本文提出一种基于时间序列的支持向量机（SVM）股票预测方法。利用沙河股份的股票数据,建立股票收盘价回归预测模型,该模型克服了传统时间序列预测模型仅局限于线性系统的情况。实验结果表明,该方法比神经网络方法以及时间序列方法的预测精度更高,可以很好的应用某些非线性时间序列的预测中。     

基于MPCA-RBF模型的证券市场指数时间序列预测*

针对证券市场指数内部结构的复杂性和影响因素的高维性,提出基于MPCA-RBF（多线性主成分分析法-径向基神经网络）模型的证券市场指数时间序列预测方法。由于证券市场间存在关联性,选取了7个证券市场及34个技术指标构建三维张量模型,采用张量方法—MPCA进行特征提取,使降维的同时充分保留数据内部结构,之后利用RBF神经网络进行回归预测,提高了预测精度。对恒生指数和日经225指数的实验结果显示,与非张量模型相比,该模型预测误差较小,预测精度有较显著的提高,表明该模型能充分地保留证券时间序列内部结构,证明了其在证券预测领域的有效性和实用性。     

一种改进的深度置信网络在交通流预测中的应用

针对现有交通流预测方法忽视对交通流数据自身特征的有效利用以及不能模拟更复杂的数学运算,提出了一种改进深度置信网络（deep belief network,DBN）的交通流预测方法。该方法结合深度置信网络模型与Softmax回归作为预测模型,利用连续受限玻尔兹曼机（continuous restricted Boltzmann machines,CRBM）处理输入特征向量,利用自适应学习步长（adaptive learning step,ALS）减少RBM训练网络模型时重建误差所需的时间,用改进的深度置信网络模型进行交通流特征学习,在网络顶层连接Softmax回归模型进行流量预测。实验结果表明,在实际的交通流数据预测中,改进的DBN模型的预测准确率以及时间复杂度相比传统预测模型都得到了较好的改善。