金融时间序列模糊边界预测研究

传统的金融时间序列预测方法以精确的输入数据为研究对象,在建立回归模型的基础上做单步或多步预测,预测结果是一个或多个具体的值.由于金融市场的复杂性,传统的预测方法可靠度较低.提出将金融时间序列模糊信息粒化成一个模糊粒子序列,运用支持向量机对模糊粒子的上下界进行回归,然后应用回归所得到的模型分别对上下界进行单步预测,从而将预测的结果限定在一个范围之内.这是一种全新的思路.以上证指数周收盘指数为实验数据,实验结果表明了这种方法的有效性.     

基于改进RNN和VAR的船舶设备故障预测方法

针对现有的多变量时间序列预测方法不能适用于船舶多设备故障预测的问题,提出一种基于改进的循环神经网络和向量自回归的船舶设备故障预测方法.该方法既能够学习多个变量之间的相互依赖关系和时间序列的长期依赖关系,又有助于减轻传统神经网络对预测时间序列的输入尺度不敏感性.首先,从船舶历史数据库中提取出正常状态数据和故障状态数据,将其多变量时间序列转化为监督学习问题的输入;然后,通过注意力机制捕获船舶多变量之间复杂的相关性;接着,将注意力机制的输出同时作为循环神经网络和向量自回归的输入,分别捕获船舶时间信号的非线性关系和线性关系;最后,将循环神经网络组件和向量自回归组件的输出进行处理后作为最终预测的结果.实验结果表明,提出的预测方法在船舶设备故障预测中训练过程的稳定性高,测试结果的均方根误差低于1.2,从而能更精确地预测船舶设备属性的趋势并避免故障的发生.     

基于改进RNN和VAR的船舶设备故障预测方法

针对现有的多变量时间序列预测方法不能适用于船舶多设备故障预测的问题,提出一种基于改进的循环神经网络和向量自回归的船舶设备故障预测方法.该方法既能够学习多个变量之间的相互依赖关系和时间序列的长期依赖关系,又有助于减轻传统神经网络对预测时间序列的输入尺度不敏感性.首先,从船舶历史数据库中提取出正常状态数据和故障状态数据,将其多变量时间序列转化为监督学习问题的输入;然后,通过注意力机制捕获船舶多变量之间复杂的相关性;接着,将注意力机制的输出同时作为循环神经网络和向量自回归的输入,分别捕获船舶时间信号的非线性关系和线性关系;最后,将循环神经网络组件和向量自回归组件的输出进行处理后作为最终预测的结果.实验结果表明,提出的预测方法在船舶设备故障预测中训练过程的稳定性高,测试结果的均方根误差低于1.2,从而能更精确地预测船舶设备属性的趋势并避免故障的发生.     

面向时序预测的支持向量回归参数选择方法

支持向量回归作为一种新的学习方法,在用于时间序列建模与预测时具有较好的泛化性能和预测能力.在支持向量回归建模的过程中,参数的选择对于模型的准确性至关重要.针对目前支持向量回归模型参数优化中存在的问题,提出一种面向时间序列预测的支持向量回归参数选择方法.根据时间序列及其预测的特点,对传统的交叉验证方法进行了改进,在保证时间序列预测方向性特征的基础上,充分挖掘有限样本所包含的信息,并将之与(-加权的支持向量回归相结合以选择好的模型参数.典型时间序列上的实验结果表明了所提出的支持向量回归参数选择方法的有效性,该方法在用于时间序列预测时取得了良好的效果.     

改进KNN的时间序列分析方法

近年来,随着数据挖掘和机器学习的兴起,基于时间序列分析方法的研究愈加丰富.作为机器学习的经典方法,KNN(K-Nearest Neighbor)因其简单、准确度高等特性被广泛应用于时间序列分析的各个领域.然而,使用原始的KNN回归方法预测时间序列具有一定的局限性,直接使用欧氏距离作为相似度度量方法的预测效果并不理想,无法适应具有整体趋势变化的时间序列的预测场景.文中提出一种拟合时间序列趋势的KNN算法TSTF-KNN(Time Series Trend Fitting KNN)算法,该方法通过对每个时刻的特征序列进行归一化处理,改进了KNN相似度度量的效果,使之可以更有效地搜索相似的特征序列.由于序列预测前进行了归一化,文中通过为预测结果添加误差项来还原序列特征,使之可以有效地预测结果.为了验证方法的有效性,从kaggle公开数据集中选取了4个数据集,并通过对这4个数据集分别进行预处理获得5个时间序列以供实验.通过使用TSTF-KNN、KNN、单层LSTM(Long Short-Term Memory)神经网络和ANN(Artificial Neural Network)在处理后的5个时间序列上进行预测实验,分析预测结果,并对比均方误差(Mean Square Error,MSE),验证了该方法的有效性.实验结果表明,该方法能有效提高KNN回归方法对时间序列预测的准确度和稳定性,使之可以更好地适应具有整体趋势变化的时间序列的预测场景.     

基于SSA和RBF的单井产量预测方法研究

单井原油产量时间序列预测对把握单井的动态情况是非常有意义的,同时它也可以为油井工作状态评估和控制提供依据.针对单井原油产量时间序列的非周期性和非线性,需要利用黑箱模型对其进行建模与预测.本文研究了结合奇异谱分析和径向基函数神经网络对单井原油产量时间序列进行多步预测的方法,证明奇异谱分析方法可以有效地减少多步预测过程中的误差传递,从而提高多步预测的可靠性.利用实测数据对该方法进行了检验,证明该方法是有利于提高预测精度的.     

多模式移动对象不确定性轨迹预测模型

以移动设备、车辆、飞机、飓风等移动对象不确定性轨迹预测问题为背景,将大规模移动对象数据作为研究对象,以频繁轨迹模式挖掘、高斯混合回归技术为主要研究手段,提出多模式移动对象轨迹预测模型,关键技术包括:1）针对单一运动模式,提出一种基于频繁轨迹模式树FTP-tree的轨迹预测方法,利用基于密度的热点区域挖掘算法将轨迹点划分成不同的聚簇,构建轨迹频繁模式树,挖掘频繁轨迹模式预测移动对象连续运动位置.不同数据集上实验结果表明基于FTP-tree的轨迹预测算法在保证时间效率的前提下预测准确性明显优于已有预测算法.2）针对复杂多模式运动行为,利用高斯混合回归方法建模,计算不同运动模式的概率分布,将轨迹数据划分为不同分量,利用高斯过程回归预测移动对象最可能运动轨迹.实验证明,相比于基于隐马尔科夫模型和卡尔曼滤波的预测方法,所提方法具有较高的预测准确性和较低的时间代价.     

基于SVM的混沌时间序列分析

支持向量机是一种基于统计学习理论的新的机器学习方法,该方法已用于解决模式分类问题．本文将支持向量机（SVM）用于混沌时间序列分析,实验数据采用典型地Mackey-Glass混沌时间序列,先对混沌时间序列进行支持向量回归实验;然后采用局域法多步预报模型,利用支持向量机对混沌时间序列进行预测．仿真实验表明,利用支持向量机可以较准确地预测混沌时间序列的变化趋势．     

基于LIBSVM和时间序列的区域货运量预测研究

针对区域货运量预测中影响因素多、样本数量小的问题,提出了互信息MI与LIBSVM支持向量回归以及状态空间时间序列相结合的预测方法,采用MI进行高维度特征降维后,以新的低维空间作为样本输入,分别建立LIBSVM支持向量回归和状态空间时间序列预测模型。通过重庆市货运量预测实验结果及对比分析表明,该方法在进行有效预测的同时能够改善预测精度,相对误差约为0.06。     

基于异常序列剔除的多变量时间序列结构化预测

针对传统多变量时间序列预测方法未考虑变量间依赖关系从而影响预测效果的问题,提出了一种基于异常序列剔除的多变量时间序列预测算法.该算法旨在利用多维支持向量回归机（Multi-dimensional support vector regression,M-SVR）内在的结构化输出特性,对选取到具有相似性的多个变量序列进行联合预测.首先,对已知序列进行基于模糊熵的层次聚类,实现对相似序列的初步划分;其次,求出类中所有序列的主曲线,根据序列到主曲线的距离计算各个序列的异常因子,从而进一步剔除聚类结果中的异常序列;最后,将选取到的相似变量序列作为输入,利用M-SVR进行预测.通过理论分析,证明本文算法在理论上存在信息损失上界与可靠度下界,从而说明本文算法的合理性与可行性.采用混沌时间序列数据与多个实际数据集进行对比实验,结果表明,与现有多个代表性方法相比,本文算法可有效挖掘多变量时间序列的内在结构信息,预测精度更高,数值稳定性更好.     

多时间尺度时间序列趋势预测

针对股票、基金等大量时间序列数据的趋势预测问题，提出一种基于新颖特征模型的多时间尺度时间序列趋势预测算法。首先，在原始时间序列中提取带有多时间尺度特征的特征树，其刻画了时间序列，不仅带有序列在各个层次的特征，同时表示了层次之间的关系。然后，利用聚类挖掘特征序列中的隐含状态。最后，应用隐马尔可夫模型（HMM）设计一个多时间尺度趋势预测算法（MTSTPA），同时对不同尺度下的趋势以及趋势的长度作出预测。在真实股票数据集上的实验中，在各个尺度上的预测准确率均在60%以上，与未使用特征树对比，使用特征树的模型预测效率更高，在某一尺度上准确率高出10个百分点以上。同时，与经典自回归滑动平均模型（ARMA）模型和PHMM（Pattern-based HMM）对比，MTSTPA表现更优，验证了其有效性。     

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.     

A model updating strategy for predicting time series with seasonal patterns

Traditional methodologies for time series prediction take the series to be predicted and split it into training, validation, and test sets. The first one serves to construct forecasting models, the second set for model selection, and the third one is used to evaluate the final model. Different time series approaches such as ARIMA and exponential smoothing, as well as regression techniques such as neural networks and support vector regression, have been successfully used to develop forecasting models. A problem that has not yet received proper attention, however, is how to update such forecasting models when new data arrives, i.e. when a new event of the considered time series occurs.This paper presents a strategy to update support vector regression based forecasting models for time series with seasonal patterns. The basic idea of this updating strategy is to add the most recent data to the training set every time a predefined number of observations takes place. This way, information in new data is taken into account in model construction. The proposed strategy outperforms the respective static version in almost all time series studied in this work, considering three different error measures.     

Real-time prediction of order flowtimes using support vector regression

In a make-to-order production system, a due date must be assigned to new orders that arrive dynamically, which requires predicting the order flowtime in real-time. This study develops a support vector regression model for real-time flowtime prediction in multi-resource, multi-product systems. Several combinations of kernel and loss functions are examined, and results indicate that the linear kernel and the ε$\epsilon$-insensitive loss function yield the best generalization performance. The prediction error of the support vector regression model for three different multi-resource systems of varying complexity is compared to that of classic time series models (exponential smoothing and moving average) and to a feedforward artificial neural network. Results show that the support vector regression model has lower flowtime prediction error and is more robust. More accurately predicting flowtime using support vector regression will improve due-date performance and reduce expenses in make-to-order production environments.     

PRTSM: Pattern recognition-based time series modeler

In this paper, a new approach using pattern recognition techniques is suggested for time series modeling which means identification of a time series into one of autoregressive moving-average models. Its main recipe is that pattern is derived from a time series and classified into a suitable model via a notion of pattern matching. The pattern is obtained from extended sample autocorrelations of the time series. The pattern recognition techniques used are learning and decision tree classifier. Learning is used in combination with linear discriminants whose goal is to discriminate one pattern from another. Decision tree classifier divides decision procedures involved in time series modeling into simpler and local decisions at each node of a decision tree. To facilitate complex tree search, knowledge-based approach is used. To implement the idea, a scheme of decision support system is employed to develop a prototype system named PRTSM (Pattern Recognition-based Time Series Modeler). Experimental results with several examples show that a pattern recognition-based approach can yield a promising solution to the time series modeling.     

Image masking for crop yield forecasting using AVHRR NDVI time series imagery

One obstacle to successful modeling and prediction of crop yields using remotely sensed imagery is the identification of image masks. Image masking involves restricting an analysis to a subset of a region's pixels rather than using all of the pixels in the scene. Cropland masking, where all sufficiently cropped pixels are included in the mask regardless of crop type, has been shown to generally improve crop yield forecasting ability, but it requires the availability of a land cover map depicting the location of cropland. The authors present an alternative image masking technique, called yield-correlation masking, which can be used for the development and implementation of regional crop yield forecasting models and eliminates the need for a land cover map. The procedure requires an adequate time series of imagery and a corresponding record of the region's crop yields, and involves correlating historical, pixel-level imagery values with historical regional yield values. Imagery used for this study consisted of 1-km, biweekly AVHRR NDVI composites from 1989 to 2000. Using a rigorous evaluation framework involving five performance measures and three typical forecasting opportunities, yield-correlation masking is shown to have comparable performance to cropland masking across eight major U.S. region-crop forecasting scenarios in a 12-year cross-validation study. Our results also suggest that 11 years of time series AVHRR NDVI data may not be enough to estimate reliable linear crop yield models using more than one NDVI-based variable. A robust, but sub-optimal, all-subsets regression modeling procedure is described and used for testing, and historical United States Department of Agriculture crop yield estimates and linear trend estimates are used to gauge model performance.     

Statistical prediction of air pollution levels using non-physical models

This paper treats the prediction problem of air pollution levels at a short range by non-physical models. Main results are given as follows: (i) The prediction accuracy of the pollution levels by time series models is compared by evaluating three performance indices, and it is shown that the multiple linear regression model already proposed is better than the auto-regressive model, the Box-Jenkins' model and the persistence model. (ii) The multiple linear regression model is more improved if the model is classified by weather. (iii) The modeling accuracy is discussed for various sample sizes, and an appropriate sample size is determined from the experiment. (iv) The confidence intervals of the predicted means at a fixed time are calculated, and the combinations of the measurement times and the measured factors that improve the prediction accuracy are chosen. (v) A revised GMDH is proposed and the accuracy by this method is more improved than those by the time series models already presented. (vi) The Kalman filtering method is applied to the prediction of pollution levels, and the measured factors that improve the prediction accuracy are chosen.     

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...     

Functional coefficient autoregressive models for vector time series

We extend the functional coefficient autoregressive (FCAR) model to the multivariate nonlinear time series framework. We show how to estimate parameters of the model using kernel regression techniques, discuss properties of the estimators, and provide a bootstrap test for determining the presence of nonlinearity in a vector time series. The power of the test is examined through extensive simulations. For illustration, we apply the methods to a series of annual temperatures and tree ring widths. Computational issues are also briefly discussed.     

基于梯度提升回归树的短时交通流预测模型

短时交通流预测是交通流建模的一个重要组成部分,在城市道路交通的 管理和控制中起着重要的作用。然而,常见的时间序列模型(如ARIMA)、随机森林(RF)模型在交通流预测方面由于被构建模型产生的残差和输入变量所影响,其预测精度受到限制。针对该问题,提出了一种基于梯度提升回归树的短时交通预测模型来预测交通速度。首先,模型引入Huber损失函数作为模型残差的处理方法;其次, 在输入变量中考虑预测断面受到毗邻空间因素和时间因素相关性的影响。模型在训练过程中通过不断调整弱学习器的权重来纠正模型的残差,从而提高模型预测的精度。利用某城市快速路的交通速度数据进行实验,并使用MSE和MAPE等指标将本文模型与ARIMA模型和随机森林模型进行对比,结果表明,文中所提模型的预测精度最好,从而验证了模型在短时交通流预测方面的有效性。