時間序列預測 的英文怎麼說

中文拼音 [shíjiānxùlièyùcè]
時間序列預測 英文
prediction of time series
  • 時 : shí］Ⅰ名1 （比較長的一段時間）time; times; days:當時at that time; in those days; 古時 ancient tim...
  • 間 : 間Ⅰ名詞1 （中間） between; among 2 （一定的空間或時間里） with a definite time or space 3 （一間...
  • 列 : Ⅰ動1 （排列） arrange; form a line; line up 2 （安排到某類事物之中） list; enter in a list Ⅱ名詞1...
  • 預 : Ⅰ副詞（預先; 事先） in advance; beforehand Ⅱ動詞（參與） take part in
  • 測 : 動詞1. （測量） survey; fathom; measure 2. （測度; 推測） conjecture; infer
  • 時間 : time; hour; 北京時間十九點整19 hours beijing time; 上課時間school hours; 時間與空間 time and spac...
  • 預測 : calculate; forecast; prognosis; divine; forecasting; foreshadowing; predetermination
  1. In, it is discussed how to estimate the profit expection and risk of portfolio by time series, and that the portfolio investment model can be made by the variance of portfolio selection random profit

    在1中，我們首先介紹了如何利用時間序列預測法估計證券的預期收益率和風險，然後以投資組合隨機收益率的方差作為投資的風險度量，建立起投資組合模型。
  2. In this paper the principle is borrowed to construct dls - icbp which realizing great improvements in prediction

    本文借用該原理實現了dls - icbp在時間序列預測上的顯著改進。
  3. In this article, we applied our improved circular back - propagation ( icbp ) network to single step and multi - steps time series prediction respectively

    在本文中將我們改進的圓形反向傳播網路模型（ improvedcircularbackpropagation - - icbp ）應用於時間序列預測，進行了單步和多步時間序列預測研究。
  4. Time - series forecasting based on neural tree model

    基於神經樹的時間序列預測
  5. Time series prediction based on emd and ann

    和神經網路的時間序列預測
  6. Forecasting method of chaotic time series based on rbf

    基於徑向基函數的海雜波時間序列預測方法
  7. A study on forecasting for time series based on wavelet analysis

    基於小波分解與重構的時間序列預測法
  8. Ridge regression learning in esn for chaotic time series prediction

    嶺回歸學習演算法及混沌時間序列預測
  9. Financial time series forecasting based on nonlinear tracking - differentiator

    微分器的金融時間序列預測
  10. Chaotic time series prediction and its applications in stock market

    混沌時間序列預測及在股票市場中的應用
  11. A method of chaotic time series prediction based on wavelet neural network

    基於小波神經網路的混沌時間序列預測
  12. Prediction modelling method for non - linear and non - stationary time series

    一種非線性非平穩時間序列預測建模方法
  13. Time series prediction model based on non - single fuzzy regular network

    基於非單點模糊正則網路的時間序列預測模型
  14. The essay combines the fuzzy mathematic concept with time - series forecast method, and gets the forecast model of total social freight turnover based on improved time - series, hereby forecasts the total social freight turnover in 2006

    摘要將模糊數學概念加入到時間序列預測方法中，得到了基於改進時間序列法的全社會貨物周轉量的預測模型，並據此預測了2006年的社會貨物周轉量。
  15. The kanerva ' s sparse distributed memory ( sdm ) tackles the problem of training large data patterns and extendes the storage mode of existing computer. but it ' s address array produced randomly ca n ' t reveal the distribution of patterns and it has ' t the ability of function approximation for its learning rule

    Kanerva的稀疏分佈存儲（ sdm ）模型解決了大維數樣本的訓練問題，推廣了現有計算機的存儲方式。但其地址矩陣的隨機預置方式不能反映樣本的分佈，並且sdm的學習方式使之不能用於函數逼近及時間序列預測問題。
  16. Based on the statistic materials of ports and shipping lines and the advice of specialists, this article analyzed the development course and status of passenger ocean transport on china - korea course, generalized the current problems. taking the port of weihai, qingdao and renchuan for example, using time series method, the author forecasted the volume of passenger on china - korea course in the next 10 years, analyzed the supply capacity and structure of passenger transport fleet, studied the constitute of the fleet, and made balance program for the passenger transport capacity on china - korea course, put forward tariff policy and competition mode of shipping companies

    本文在調查收集港航各方面的統計資料，聽取有關專家的意見的基礎上，分析了中韓航線海上客運的發展歷程和現狀，總結了現在存在的問題；運用時間序列預測法，選取威海、青島和仁川為例，對山東半島至韓國間的客滾運輸航線的客、貨運輸進行了今後10年該航線的客流量預測；分析了客運船隊運力供給及其結構，研究了當前船隊構成存在的問題，作出了中韓航線客運運力的平衡規劃；提出航運公司的運價策略，運輸競爭模式。
  17. At last, compared the three data extention technologys merits. the time - series based and neuron network based data extention methods were proposed. chapter four studied the improvement of the emd algorithm al efficiency and precision. reserthed the effect of spline interpolation ' s end condition to the emd algorithm ' s precision, then discussed the low - order and propoed high - order spline interpolation based emd algorithm and their effects

    第三章介紹了emd演算法端點效應的機理；然後系統地研究了直接信號序列延拓技術、基於時間序列預測和基於神經網路預測的數據序列延拓技術的特點及性能；最後，對各種延拓技術進行了比較研究，分析了各種延拓技術的優缺點。
  18. Primary discussion on the time series prediction of the leaching rate of in - situ blasting and leaching ore

    原地爆破浸出率的時間序列預測問題初探
  19. Analysis on the predictive model of the time series of cancer mortality in liwan district, guangzhou

    廣州市荔灣區惡性腫瘤死亡率時間序列預測模型分析
  20. Consequencely, the analysis and assessment system of the operating state of the concrete dam was developed under the ansys development environment, and the back - analysis and the prediction of the parameters of the dam system have been done according to the operating observed data. the research work involves several aspects as follows : 1 ) a back - analysis model including the prior information and a predictive model of nonlinear time series were established ; 2 ) the analysis and assessment system of the operating state of the concrete dam was developed under the ansys development environment, in which the analysis module of the loading effects, the back - analysis module, the forecasting module of the physical parameters and the assessment module of the operating state were contained ; 3 ) according to the operating observed data, the operating state of the dam was assessed and predicted with the analysis system of the operating state, the results suggested that the dam is in basically normal operation at present

    具體工作有以下幾方面： 1 ）收集整理漫灣水電站大壩運行監測資料，並對其進行了系統分析，從觀測資料本身直觀的分析了大壩的運行狀況； 2 ）總結位移反分析的理論及方法，建立了考慮先驗信息多介質位移反分析模型，基於神經網路非線性映射功能，建立了神經網路時間序列預測模型； 3 ）以ansys軟體為平臺，開發了漫灣水電站砼重力壩運行狀態評價模塊，結合荷載效應分析、參數反演分析、參數預測分析三個模塊組成漫灣水電站砼重力壩運行狀態分析系統；實現了對漫灣水電站運行狀態的動態「反演-預測」分析； 4 ）利用漫灣水電站砼重力壩運行狀態分析系統，在漫灣大壩實測資料分析、大壩砼特性參數反分析、大壩砼彈性模量衰變規律及預測分析的基礎上，系統的分析並預測了大壩運行狀態。
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