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基于支持向量回归(SVR)进行金融时间序列预测,使用PSO算法确定SVR超参数,并用实验的方法选择合适的SVR输入向量。为了解决金融时间序列非平稳性导致的单一SVR模型预测精度不稳定的问题,提出一种混合多个SVR模型的预测算法,选取训练数据的不同子集训练出多个SVR模型,采用对多个模型的预测结果加权求和的方法进行预测,各个模型的权重根据其预测误差动态调整。在全球5大股指上的实验表明,该算法的预测能力明显优于单一SVR模型。 相似文献
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模型参数的估计是时间序列预测研究的重要内容,经典模型参数估计方法对参数估计结果不进行评估和优化,预测精度往往达不到要求。本文提出基于遗传算法进行模型参数估计的方法,该方法具备了群集思想和递推思想的特征,仿真实验表明所提出算法相比传统算法,具有更高的预测精度。 相似文献
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城市交通拥堵情况预测序列多为单层级处理,导致预测结果误差较大,为此提出基于时间序列模型的城市交通拥堵情况预测方法。首先,进行预测指标选取及数据采集,采用多目标的方式扩大预测范围;其次,设计多目标预测序列,进行交通拥堵状态判别及无量纲化处理;最后,构建时间序列交通拥堵情况预测模型,并进行对比分析。测试结果表明,该预测方法的针对性较强,能够将预测绝对误差控制在1.5以下,具有实际应用价值。 相似文献
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奚婧 《电脑与微电子技术》2014,(19):9-14
由于黄金价格受到诸多经济及政治因素的影响,其生成过程十分复杂,因此研究黄金价格的动态演变过程具有极强的现实意义。而具体的预测要根据市场的过去和当前的需求,以及影响市场需求变化的因素之间的关系,利用一定的判断、技术和模型,对其价格波动变化及发展趋势进行分析和判断。利用时间序列相关理论,建立黄金价格的ARMA模型,发现其可以动态刻画黄金价格数据的生成过程并且较好地对黄金价格进行预测。 相似文献
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时间序列分析是经济领域应用研究最广泛的工具之一,它用恰当的模型描述历史数据随时间变化的规律,并分析预测变量值。ARMA模型是一种最常见的重要时间序列模型,它被广泛应用到经济领域预测中。本文给出ARMA模型的三种模式和实现方法,然后结合超市销售数据揭示超市销售的规律性,并运用ARMA模型对超市销售量进行预测。 相似文献
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基于一种时间序列模型的河流重金属污染浓度预测研究 总被引:1,自引:0,他引:1
水环境是一个充满不确定性的复杂巨系统,传统水质模型很难体现重金属污染物在河流中迁移的随机性,因此经典的时间序列模型——ARIMA模型被应用于河流重金属污染浓度的预测。实例分析证实,通过采用将荻得的最新数据不断地添加到用于模型设定的样本中,并再此基础上获得最近向前一个时期预测值的动态预测方法,ARIMA模型能够获得很好的预测表现,尤其是在充分考虑模型残差统计分布特征的情况下,采用具有学生t分布的模型预测更精确。 相似文献
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为在实时电价情况下预测未来24小时电价, 提出一种基于小波变换和差分自回归移动平均(ARIMA)的短期电价混合预测模型。该模型分别根据是否受到需求量影响使用ARIMA模型对多尺度小波变换分解后的时间序列进行预测。同时提出一种电价突变点发现和处理算法。使用澳大利亚新南威尔士州2012年真实数据验证表明, 相对ARIMA预测, 改进后的混合模型在不考虑需求量影响时预测精度更高; 电价突变点发现和处理算法能够准确处理电价异常点, 提高预测精度。 相似文献
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Xiao LiuAuthor Vitae Zhiwei NiAuthor VitaeDong YuanAuthor Vitae Yuanchun JiangAuthor Vitae Zhangjun WuAuthor Vitae Jinjun ChenAuthor VitaeYun YangAuthor Vitae 《Journal of Systems and Software》2011,84(3):354-376
Forecasting workflow activity durations is of great importance to support satisfactory QoS in workflow systems. Traditionally, a workflow system is often designed to facilitate the process automation in a specific application domain where activities are of the similar nature. Hence, a particular forecasting strategy is employed by a workflow system and applied uniformly to all its workflow activities. However, with newly emerging requirement to serve as a type of middleware services for high performance computing infrastructures such as grid and cloud computing, more and more workflow systems are designed to be general purpose to support workflow applications from many different domains. Due to such a problem, the forecasting strategies in workflow systems must adapt to different workflow applications which are normally executed repeatedly such as data/computation intensive scientific applications (mainly with long-duration activities) and instance intensive business applications (mainly with short-duration activities). In this paper, with a systematic analysis of the above issues, we propose a novel statistical time-series pattern based interval forecasting strategy which has two different versions, a complex version for long-duration activities and a simple version for short-duration activities. The strategy consists of four major functional components: duration series building, duration pattern recognition, duration pattern matching and duration interval forecasting. Specifically, a novel hybrid non-linear time-series segmentation algorithm is designed to facilitate the discovery of duration-series patterns. The experimental results on real world examples and simulated test cases demonstrate the excellent performance of our strategy in the forecasting of activity duration intervals for both long-duration and short-duration activities in comparison to some representative time-series forecasting strategies in traditional workflow systems. 相似文献
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当研究的系统扰动因素过大或系统行为在某个时川点发生突变,出现严重扰动系统的异常数据时,提出不应直接按原始数据建模预测,而应根椐实际情况适当地对数据预处理.提出了基于数据修正的改进型灰色神经网络组合和集成预测,并根据南昌火车站旅客发送量时间序列建立了多个模型,从模型预测效果对比中说明数据修正、改进型灰色模型和改进型灰色神经网络、灰色神经网络组合和集成确实能提高预测精度.另外,修正数据要把握一个度,不能修正全部数据,只能修正较异常的数据,要在数据的趋势性和预测的灵敏性间取得平衡。 相似文献
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In this study, a new kind of fuzzy set in fuzzy time series’ field is introduced. It works as a trend estimator to be appropriate for fuzzy time series forecasting by reconnoitering trend of data appropriately. First, the historical data are fuzzified into differential fuzzy sets, and then differential fuzzy relationships are calculated. Second, differential fuzzy logic groups are established by grouping differential fuzzy relationships. Finally, in the defuzzification step, the forecasts are calculated. However, for increasing the accuracy of the models, an evolutionary algorithm, namely imperialist competitive algorithm is injected, to train the model. A massive stock data from four main stock databases have been selected for model validation. The final project, has shown that outperformed its counterparts in term of accuracy. 相似文献
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Fuzzy time series model has been successfully employed in predicting stock prices and foreign exchange rates. In this paper, we propose a new fuzzy time series model termed as distance-based fuzzy time series (DBFTS) to predict the exchange rate. Unlike the existing fuzzy time series models which require exact match of the fuzzy logic relationships (FLRs), the distance-based fuzzy time series model uses the distance between two FLRs in selecting prediction rules. To predict the exchange rate, a two factors distance-based fuzzy time series model is constructed. The first factor of the model is the exchange rate itself and the second factor comprises many candidate variables affecting the fluctuation of exchange rates. Using the exchange rate data released by the Central Bank of Taiwan, we conducted several experiments on exchange rate forecasting. The experiment results showed that the distance-based fuzzy time series outperformed the random walk model and the artificial neural network model in terms of mean square error. 相似文献
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《国际计算机数学杂志》2012,89(7):781-789
This article presents an improved method of fuzzy time series to forecast university enrollments. The historical enrollment data of the University of Alabama were first adopted by Song and Chissom (Song, Q. and Chissom, B. S. (1993). Forecasting enrollment with fuzzy time series-part I, Fuzzy Sets and Systems, 54, 1–9; Song, Q. and Chissom, B. S. (1994). Forecasting enrollment with fuzzy time series-part II, Fuzzy Sets and Systems, 54, 267–277) to illustrate the forecasting process of the fuzzy time series. Later, Chen proposed a simpler method. In this article, we show that our method is as simple as Chen's method but more accurate. In forecasting the enrollment of the University of Alabama, the root mean square percentage error (RMSPE) of our method is 3.1113% while the RMSPE of Chen's method is 4.0516%, which shows that our method is doing much better. 相似文献
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针对目前仅单独考虑价格序列中样本的趋势或仅考虑多个关联属性与价格间的函数关系,而不能更准确地进行房价预测的问题,构建了时空注意力图卷积长短期记忆模型AG-LSTM,包含局部特征提取模块、区域特征提取模块、复合预测模块。局部特征提取模块分别使用同构图和异构图神经网络提取各小区及价格关系属性、各小区和配套邻居节点相关性的特征信息;区域特征提取模块先对邻近小区节点进行聚类,再结合图注意力网络获得小区节点对所属区域的重要性程度,建立区域与小区之间的映射矩阵,根据小区节点信息和映射矩阵得到区域特征;复合预测模块使用长短期记忆模型对由局部特征和区域特征组成的复合特征进行时序建模,实现房价预测。以链家网北京房价数据进行了实验,结果表明AG-LSTM预测结果优于已有基线模型。该模型同时挖掘了小区间位置关系、小区与其配套间位置关系、多个关联属性、价格时序趋势对房屋价格的影响,较好地实现了房屋价格的预测。 相似文献
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提出一种基于梯度下降法的混合进化算法,用于确定径向基函数(RBF)神经网络结构和优化其参数.在进化算法中嵌入梯度下降算子,对每一代中若干个精英个体以一定概率利用梯度下降法进行搜索,以加强算法的局部搜索能力.利用混合进化算法对RBF网络结构和参数同时进行训练和优化,对网络节点数和参数进行混合编码.仿真实验结果表明该RBF网络具有较强的泛化能力. 相似文献