小波神经网络在房地产价格指数预测中的应用

随着房地产价格指数的作用充分显现,探求预测房地产价格指数的有效方法是需深入研究的方向。该文以中房上海住宅价格指数为例,首先对房地产价格指数序列性质进行分析,表明房地产价格指数是具有非线性特征的非平稳时间序列。采用小波神经网络对房地产价格指数进行预测,并将预测结果与指数平滑法和RBF神经网络预测做了对比。采用MATLAB对拟合和预测过程进行仿真。结果指标表明,在大样本数据的情况下,采用小波神经网络对房地产指数进行预测能够获得较好的效果。     

一种指数平滑预测的参数优化方法及实现

时间序列预测法在各种基于时态数据库的计算中有着广泛的应用前景.文中介绍了时间序列预测法中的单指数平滑、双指数平滑和三指数平滑三种指数平滑预测方法,不同的预测方法适合于对不同时间特性的数据、平稳性数据、趋势性数据或季节波动性数据进行预测,使用相应的预测方法均达到很好的平滑效果.同时还介绍了如何应用1Gs算法对指数平滑的多数进行优化,从而得到更好的平滑效果和预测结果,使之在社会实际当中发挥更好的作用.     

一种指数平滑预测的参数优化方法及实现

时间序列预测法在各种基于时态数据库的计算中有着广泛的应用前景。文中介绍了时间序列预测法中的单指数平滑、双指数平滑和三指数平滑三种指数平滑预测方法，不同的预测方法适合于对不同时间特性的数据、平稳性数据、趋势性数据或季节波动性数据进行预测，使用相应的预测方法均达到很好的平滑效果。同时还介绍了如何应用IGS算法对指数平滑的参数进行优化，从而得到更好的平滑效果和预测结果，使之在社会实际当中发挥更好的作用。     

基于小波分解和聚类模糊系统的时间序列预测

结合小波分析的多分辨特性和模糊规则的可解释性,提出了一种非平稳时间序列预测方法。首先将时间序列信号分解为各尺度上的细节信号和平滑信号,使用软、硬阈值折衷法消除噪声,并对各层重构信号单独使用聚类模糊系统进行预测,最后将各预测结果累加得到对整个时间序列的预测。仿真试验表明此方法是有效的。     

基于遗传退火三次指数平滑的期货预测研究

为更准确地对期货价格进行预测分析,提出了一种对三次指数平滑法的平滑初值和平滑参数值进行自适应选取的预测方法.该方法根据样本预测误差平方和最小来自适应地调整平滑参数与平滑初值,以避免对其进行主观选取,并将此方法应用于期货价格的预测中.实验结果表明,该方法与单独的三次指数平滑法相比,对于期货价格预测的准确率有所提高,可以得到比较有效的预测值.     

时间序列预测的动态神经网络方法

时间序列的预测在经济和工程领域具有十分重要的意义。文中利用动态神经网络的特性,提出对时间序列进行预测的动态神经网络方法,并利用设计的动态神经网络对杜芬(Duffing)方程的响应时间序列进行预测,结果表明动态神经网络可以较好地对动态系统的响应时间序列进行预测。     

时间序列预测的动态神经网络方法

时间序列的预测在经济和工程领域具有十分重要的意义.文中利用动态神经网络的特性,提出对时间序列进行预测的动态神经网络方法,并利用设计的动态神经网络对杜芬(Duffing)方程的响应时间序列进行预测,结果表明动态神经网络可以较好地对动态系统的响应时间序列进行预测.     

基于小波神经网络的IP网络流量预测

采用小波神经网络对网络流量数据的时间序列进行建模与预测。针对BP神经网络预测准确率不太理想的情况,将小波理论引入BP神经网络,引用小波理论中多分辨分析技术对基于BP神经网络的模型进行改进,建立了基于小波神经网络的IP网络流量预测模型。该模型利用小波多分辨分析分解信号,再用已分解的信号序列来训练BP神经网络。实验结果表明,小波神经网络比BP神经网络对网络流量的预测结果精度更高、性能更好,利用小波神经网络预测网络流量是一种可行、有效的方法。     

基于ELM-PNN算法的第24周太阳黑子预测预报

为了提高太阳黑子预测预报的精度,提出固定型极限学习过程神经网络(FELM-PNN)和增量型极限学习过程神经网络(IELM-PNN)两种学习算法.FELM-PNN的隐层节点数目固定,使用SVD求解隐层输出矩阵的Moore-Penrose广义逆,通过最小二乘法计算隐层输出权值;IELM-PNN逐次增加隐层节点,根据隐层输出矩阵和网络误差计算增加节点的输出权值.通过Henon时间序列预测验证了两种方法的有效性,并实际应用于第24周太阳黑子平滑月均值的中长期预测预报中.实验结果表明,两种方法的预测精度均有一定程度的提高,IELM-PNN的训练收敛性优于FELM-PNN.     

基于小波神经网络的数据中心KPI预测

时间序列预测是数据中心关键性能指标异常检测的重要环节。针对时间序列,利用小波基函数作为隐含层节点传递函数来构造小波神经网络进行预测;同时选取动量梯度下降法提高神经网络学习效率;再根据粒子群算法训练得到最优解作为神经网络参数初始值;最后使用MATLAB进行仿真,以较高准确性对关键性能指标时间序列进行了预测。     

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.     

Hybrid neural network models for hydrologic time series forecasting

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.     

基于非线性组合模型的交通流预测方法

为开发智能交通系统,提出一种基于RBF和ARIMA网络非线性组合模型的短时交通流预测方法,采用三层结构的RBF网络将2种单一预测方法——RBF和ARIMA网络进行非线性组合,利用实测数据对3类方法进行仿真实验,结果表明,非线性组合模型的预测准确性高于各自单独使用时的准确性,组合模型发挥了2种单一方法各自的优势,是短时交通流预测的有效方法。     

交通流时间序列分离方法

采用聚类分析方法对交通流时间序列进行分析可以发现典型的交通流变化模式。通常 可采用欧式距离及K均值算法进行时间序列聚类,但经分析发现单凭此方法还难以实现不同变化趋 势的交通流时间序列的有效分离。针对此问题,提出了将动态时间弯曲及灰色关联度引入交通流时 间序列相似性度量,且结合层次化聚类方法对交通流时间序列进一步分离的方法。通过实验研究,发 现基于灰色关联度的层次化聚类方法能较好地实现交通流时间序列的进一步有效分离。     

Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.     

Integration of multi-time-scale models in time series forecasting

A solution to the problem of producing long-range forecasts on a short sampling interval is proposed. It involves the incorporation of information from a long sampling interval series, which could come from an independent source, into forecasts produced by a state-space model based on a short sampling interval. The solution is motivated by the desire to incorporate yearly electricity consumption information into weekly electricity consumption forecasts. The weekly electricity consumption forecasts are produced by a state-space structural time series model. It is shown that the forecasts produced by the forecasting model based on weekly data can be improved by the incorporation of longer-time-scale information, particularly when the forecast horizon is increased from 1 year to 3 years. A further example is used to demonstrate the approach, where yearly UK primary fuel consumption information is incorporated into quarterly fuel consumption forecasts.     

基于改进时空残差卷积神经网络的城市路网短时交通流预测

城市路网交通流预测受到历史交通流和相邻路口交通流的影响,具有复杂的时空关联性.针对传统时空残差模型缺乏对交通流数据进行相关性分析、捕获微小变化而容易忽略长期时间特征等问题,提出一种基于改进时空残差卷积神经网络(CNN)的城市路网短时交通流预测模型.该模型将原始交通流数据转化成交通栅格数据,利用皮尔逊相关系数(PCC)对...     

基于组合模型的短时交通流预测方法

准确、实时的交通流预测对交通规划、交通管理和交通控制具有重要意义.然而,由于道路网络拓扑结构约束和交通流随时间动态变化的空时相关特性,交通流预测仍然具有挑战性.为了同时捕获交通流的空间和时间相关性,提出一种将图卷积网络(GCN)和门控循环单元(GRU)相结合的组合模型方法.利用GCU能够灵活处理图结构数据的优点来捕捉各个路段的空间特征,继而发挥GRU在处理时间序列方面的优势挖掘交通流的内在时间规律,空时融合后得到最终预测结果.利用美国交通研究数据实验室的高速公路交通数据对该模型进行仿真验证,结果表明,所提出的GCN-GRU组合模型方法具有更高的预测精度,预测结果优于自回归积分滑动平均(ARIMA)模型和GRU模型等基准预测方法.     

无线局域网业务流预报方法比较

介绍了基于时间序列、神经网络和小波的多种网络业务的预报方法,应用真实的无线局域网业务流序列检验了这些模型的预报性能,结果表明,和其他预报模型相比,基于神经网络的模型能够比较精确地捕获无线局域网业务流自身的特性,对业务流具有良好的预报性能,而基于ARIMA模型的预报性能最差。     

A new architecture selection method based on tabu search for artificial neural networks

Artificial neural networks (ANN) have been used in various applications in recent years. One of these applications is time series forecasting. Although ANN produces accurate forecasts in many time series implementations, there are still some problems with using ANN. ANN consist of some components such as architecture structure, learning algorithm and activation function. These components have important effect on the performance of ANN. An important decision is the selection of architecture structure that consists of determining the numbers of neurons in the layers of a network. Therefore, various approaches have been proposed to determine the best ANN architecture in the literature. However, the most preferred method is still trial and error method for finding a good architecture. In this study, a new architecture selection method based on tabu search algorithm is proposed. In the implementation, five real time series are analyzed by using ANN and the proposed method is employed to select the best architecture. For the comparison, these time series are also forecasted by using ANN when trial and error method is utilized to determine the best architecture. As a result of the implementation, it is clearly seen that better results are obtained when the proposed method is used for the selection of architecture.