共查询到18条相似文献，搜索用时 281 毫秒
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均生函数预报模型在西藏汛期旱涝预报中的应用 总被引：3，自引：0，他引：3
以给定的时间序列，通过正交化处理，利用双评分准则筛选均生函数，建立均生函数预报模型，并把几个最优周期的均生函数叠加后得到31年降水量的拟合序列，建立了全区降水、气温预报系统。用均生函数分别为西藏23个站点。初夏（5－6月），盛夏（7－8月），夏季（5－9月）降水量进行了模拟延延，试报1999年，2000年，2001年降水趋势。通过单站预报结果，分析大范围气候趋势，并综合其他预报方法，确定当年旱涝趋势预测。 相似文献
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关联分析法在周期外延预测中的应用程毛林（扬州大学税务学院会计系２２５００２）１引言对呈一定周期波动的时间序列进行分析有两个重要目的，一是提取时间序列的优势周期；二是建立拟合厅列的最佳数学模型，并用此模型预测，在长期预测中通常用筛选均生函数的方法建立预... 相似文献
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伊犁新垦区土壤全盐量和电导率定量关系探讨 总被引：3，自引：0，他引：3
土壤含盐量和电导率是调查评价土壤盐渍化程度的两种指标,进行两者关系的定量探讨是解决土壤盐渍化快速诊断问题的前提。本文以伊犁新垦区为例,采用传统的一次函数、二次函数和分段函数等对土壤含盐量和电导率关系进行回归拟合,同时应用土地利用类型、植被类型和土壤类型等环境因子作为辅助数据对两者进行分区线性拟合,并分析比较两种方法的优劣。结果表明,随着全盐量的增加,土壤全盐量和电导率呈现一定的非线性关系,以土壤全盐量为阈值的分段函数拟合效果优于一次函数和二次函数,其RMSE小于后两者;以土地利用类型作为辅助数据进行的分组拟合结果优于植被类型和土壤类型的效果,也略优于分段函数的拟合精度。该方法在宏观尺度上为快速、精确获取土壤盐分状况提供帮助。 相似文献
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DNA序列分析法在金融数据时间序列中的应用 总被引：5，自引：0，他引：5
通过线性分段将连续性的金融时间序列转化为离散性的字符序列,并基于DNA序列分析法,讨论了此类字符序列的标度特性,以及在金融数据时间序列预测中的可能应用 相似文献
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提出用遗传算法优化径向基函数(RBF)神经网络,使其更接近非线性映射和更快的学习收敛速度.然后用改进后的RBF神经网络预测混沌时间序列.实验结果表明,基于RBF网络的混沌时间序列具有很强的拟合能力、误差小、取得更好的效果. 相似文献
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Panel Data与前沿生产函数 总被引：2，自引：0，他引：2
ＰａｎｅｌＤａｔａ与前沿生产函数李致平（华东冶金学院２４３００２）１引言ＰａｎｅｌＤａｔａ是将时间序列（ＴｉｍｅＳｅｒｉｅｓ，简称ＴＳ）和截面（ＣｒｏｓｓＳｅｒｉｅｓ，简称ＣＳ）数据相结合建立计量模型的一种数理统计方法。ＰａｎｅｌＤａｔａ方法被用于解... 相似文献
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针对径流时间序列的非平稳特性及中长期预测精度低的问题,本文提出一种新的耦合预测方法：基于EMD分解的均生函数-最优子集回归（Mean Generating Function-Optimum Subset Regression,MGF-OSR）模型。首先利用经验模态分解（Empirical Mode Decomposition,EMD）方法对汾河上游上静游、汾河水库、寨上和兰村4座水文站的年径流序列进行平稳化处理,分别得到若干个固有模态函数（Intrinsic Mode Function,IMF）。对各阶固有模态函数分别建立MGF-OSR模型并进行预测,趋势项用直线拟合的方法进行预测,然后通过重构各预测值得到汾河上游4座水文站年径流量的预测结果,并与单独运用MGF-OSR模型的预测结果进行比较。结果表明,运用基于EMD分解的MGF-OSR模型对汾河上游4站年径流进行预测,准确率均为100%,确定性系数在0.975以上;而单一模型的预测准确率均为40%,确定性系数在0.732以下,耦合模型预测精度明显提高。 相似文献
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《Journal of The Franklin Institute》2023,360(5):3575-3591
In this paper, we study the problem of remote state estimation on networks with random delays and unavailable packet sequence due to malicious attacks. Two maximum a posteriori (MAP) schemes are proposed to detect the unavailable packet sequence. The first MAP strategy detects the packet sequence using data within a finite time horizon; the second MAP strategy detects the packet sequence by a recursive structure, which effectively reduces the computation time. With the detected packet sequence, we further design a linear minimum mean-squared error (LMMSE) estimation algorithm based on smoothing techniques, rather than using the classic prediction and update structure. A wealth of information contained in the combined measurements is utilized to improve the estimation performance. Finally, the effectiveness of the proposed algorithms is demonstrated by simulation experiments. 相似文献
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《Journal of The Franklin Institute》2019,356(15):8906-8928
Selection of optimal dimension of trajectory matrix in singular spectrum analysis plays an important role in signal reconstruction from noisy time series. A noisy time series is embedded into a Hankel matrix and the dimension of this matrix depends on the window length considered for a time series. The window length requirement of a time series depends on its underlying data generating mechanism. Since the number of columns in a Hankel structured trajectory matrix is a function of number of rows (window length), dimension dependency occurs naturally in the trajectory matrix and this dependency is characterized by the statistical properties of a time series. In this paper, we develop an entropy based dimension dependency measure that accounts for changes in information content in the matrix in response to changes in window length for a time series. We examine the performance of this measure by using simulation experiments and analyzing real data sets. Results obtained from simulation experiments show that the dimension dependency measure finds reasonably meaningful dimension of the trajectory matrix and provides better forecasting outcome when applied to some popular climatic time series and production indices. 相似文献
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《Information processing & management》2023,60(3):103328
Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system’s internal dynamics. For instance, the driving force (state) behind the stock rise may be the company’s good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market’s pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline. 相似文献
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This paper studies the distributed Kalman consensus filtering problem based on the event-triggered (ET) protocol for linear discrete time-varying systems with multiple sensors. The ET strategy of the send-on-delta rule is employed to adjust the communication rate during data transmission. Two series of Bernoulli random variables are introduced to represent the ET schedules between a sensor and an estimator, and between an estimator and its neighbor estimators. An optimal distributed filter with a given recursive structure in the linear unbiased minimum variance criterion is derived, where solution of cross-covariance matrix (CCM) between any two estimators increases the complexity of the algorithm. In order to avert CCM, a suboptimal ET Kalman consensus filter is also presented, where the filter gain and the consensus gain are solved by minimizing an upper bound of filtering error covariance. Boundedness of the proposed suboptimal filter is analyzed based on a Lyapunov function. A numerical simulation verifies the effectiveness of the proposed algorithms. 相似文献
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Goktug T. Cinar Pedro M.N. Sequeira Jose C. Principe 《Journal of The Franklin Institute》2018,355(4):1638-1662
In this paper we develop a new framework for time series segmentation based on a Hierarchical Linear Dynamical System (HLDS), and test its performance on monophonic and polyphonic musical note recognition. The center piece of our approach is the inclusion of constraints in the filter topology, instead of on the cost function as normally done in machine learning. Just by slowing down the dynamics of the top layer of an augmented (multilayer) state model, which is still compatible with the recursive update equation proposed originally by Kalman, the system learns directly from data all the musical notes, without labels, effectively creating a time series clustering algorithm that does not require segmentation. We analyze the HLDS properties and show that it provides better classification accuracy compared to current state-of-the-art approaches. 相似文献
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【目的/意义】当前的信息用户行为特征挖掘方法无法将数据统一整合,且无法准确计算出时间序列内滑动
窗口内的数据均值,导致特征挖掘精度偏低。为此,提出了基于时间特性的信息用户行为特征挖掘方法。【方法/过
程】计算时间序列内滑动窗口内的数据均值,得出起始序列向量,再将用户行为划分成若干等值的时间片,通过取
样统计各种用户群体,得出用户的行为状态定性。以平均查询频率作为标准,观察用户的查询行为特征,输出信息
挖掘结果。【结果/结论】实验结果表明：所提方法挖掘出夜晚用户行为信息多于白天,休息日比工作日多,且在网络
波动下,虽然耗时增加,不过处于合理范围内。与传统方法相比,所提方法具有更低的挖掘误差,应用性较强。以
上实验结果证明了基于时间特性的信息用户行为特征挖掘研究能获取更准确的用户行为意向,提高用户兴趣预测
准确度,优化网络服务效果。【创新/局限】为进一步提高网络信息特征挖掘的效率,后续将重点研究多个网络用户
行为的并行分析,使该方法更适用于网络海量信息处理。 相似文献
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Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time series data and an LSTM Autoencoder network-based method combined with a one-class support vector machine algorithm for detecting anomalies in sales. Unlike other approaches, we recommend combining external and internal company data sources for the purpose of enhancing the performance of forecasting algorithms using multivariate LSTM with the optimal hyperparameters. In addition, we also propose a method to optimize hyperparameters for hybrid algorithms for detecting anomalies in time series data. The proposed approaches will be applied to both benchmarking datasets and real data in fashion retail. The obtained results show that the LSTM Autoencoder based method leads to better performance for anomaly detection compared to the LSTM based method suggested in a previous study. The proposed forecasting method for multivariate time series data also performs better than some other methods based on a dataset provided by NASA. 相似文献
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目前国内外已有的创新指数主要是基于创新能力与创新绩效测度的,而且全部是年度数据,存在如下缺点:动态性和时效性不足,指标宽泛,指标间的时序性缺乏,而且无预警功能,不能及时反映区域的科技创新活动景气状态,对科技创新政策的引导作用不强。本文从测度科技创新景气状态的视角出发,基于熊彼特的创新周期理论,研究了区域科技创新景气指数构建的理论基础,探讨了基于合成指数构建科技创新景气指数的方法,并构建了一套基于季度数据测度的深圳南山区科技创新景气指数。研究表明基于季度数据合成的科技创新景气指数可以及时、动态地反映区域科技创新的景气状况,预测科技创新的发展趋势,这对区域科技创新政策的制定、反馈、科技资源的配置有很大的指导意义。 相似文献