moving average model 中文意思是什麼

moving average model 解釋
移動平均模型
  • moving : n. 1. 活動,移動；煽動,感動。2. 〈pl. 〉〈口語〉電影。adj. 1. 動的；移動的。2. 使人感動的,動人的。3. 主動的，原動力的。
  • average : n 1 平均，平均數。2 一般水平，平均標準。3 【商業】海損；海損費用；（給領航的）報酬。adj 1 平均的...
  • model : n 1 模型，雛型；原型；設計圖；模範；（畫家、雕刻家的）模特兒；樣板。2 典型，模範。3 （女服裝店僱...
  1. The forecasts method including the forecast method of simple moving average, the forecast method of weighting moving average, the forecast method of single exponential smoothing, the forecast method of double exponential smoothing, the forecast method of multiplication model and the forecast method of monadic linear regression

    預測方法包括簡單移動平均法、加權移動平均法、一次指數平滑法、二次指數平滑法、乘法模型預測法和一元線性回歸方程預測法。
  2. Moreover, special aspects of self - similar traffic are summarized. for long - range dependent traffic, two prediction models are given and discussed the prediction results can be applied to reduce loss ratio in allocation of memories in network nodes. the first model is farima ( fractional autoregressive integrated moving average )

    根據自相似業務流的長相關特性，本文重點討論了兩種數學模型，目的是用這兩種模型對自相似業務流進行預測，進而根據預測結果對計算機網路節點的存儲器資源進行合理的分配，使得丟失率達到最小。
  3. This system adopts cumulatively autoregressive moving average model [ arima ] of time series method and modified model gm ( 1, 1 ) of grey system, makes a local load forecasting modeling through the integration of the above two models and also preprocesses the daily load during the sudden change of climate, thus greatly improving the forecast accuracy. the practical operation indicates that the model is reasonable and easy to operate with complete function

    本系統在經過反復試算后，在演算法上採用了時間序列法的累積式自回歸動平均模型（ arima ）與灰色系統中的gm （ 1 ， 1 ）改進模型，並將兩種模型組合用於該地區負荷預報建模，另外還對氣候急變日負荷進行了預處理，大大提高了預報準確度。
  4. Based on the three prediction models, the combined forecast is conducted using the separate solutions of moving average, exponential smoothing and gray prediction to investigate whether the combined forecast model matches the sales volume prediction of truck wooden boxes

    最後採用組合預測之三種預測模型，將移動平均法、指數平滑法、灰預測法所求得之解作組合預測，以探討組合預測模型是否適合木製框式車身的銷售數量預測。
  5. Dynamic weighing system is as a second - order system and set it up model, then has its transform function laplace transform and z transform, at last has a formula that m is only relation to the system parameters. this article has system identified with the recursive least square ( rls ) method, and has the system parameters, while the auto - regressive - moving - average ( arma ) model for the second order weighing system is firstly derived. and has a equation which the mass is only correlation to the system parameters

    論文具體分析了定量稱量問題，首先是把稱量系統看作是一個二階系統，建立數學模后，進行拉普拉斯變換和z變換后得出一個質量僅與系統參數有關的關系式，從而把稱量問題轉化為一個系統參數識別問題來解決。通過編寫的程序來採集系統信號並進行處理（運用漸消遞推最小二乘法）對系統參數進行識別，從而得出稱量結果。
  6. This part mainly discusses the statistical distribution of the price and the returns rate, including random process and the returns rate model, gaussian process, measuring returns rate with discrete random process, white noise process, auto regression process, moving average process, auto regression moving average process, random walk, continuous random process, leptokurtic distribution, conditional mixed distribution, garch model and fractal distribution

    在這一部分中，我們主要討論價格和收益率的統計分佈：隨機過程和收益率模型、高斯過程、收益率計量中的離散隨機過程、白噪聲過程、自回歸過程、移動平均過程、自回歸移動平均過程、隨機行走、連續隨機過程、尖峰分佈、條件混合分佈、 garch模型以及分形分佈。
  7. Exponentially weighted moving average ( ewma ) and fuzzy algorithm for the input samples are also developed to improve its recognition accuracy. numerical simulation results show this model possesses many advantages, such as good self - adaptive ability, quick training and good recognition performance

    文中提出了採用歐氏距離判別法作為混合型多特徵異常模式的識別方法；提出了採用數據模糊化和指數加權滑動平均處理兩種提高模型識別精度的方法。
  8. According to the needs of gps / sins integrated navigation algorithm, the error models of gps and sins are studied respectively. the autoregressive ( ar ) models and autoregressive moving average ( arma ) models of gps positioning error are established based on the analysis of the properties of static gps positioning error data. and the neural network method to determine the ar model parameters is given

    根據gps / sins組合導航演算法的需要，分別對gps和捷聯系統的誤差模型進行了研究，在對gps靜態定位誤差數據特性分析的基礎上，建立了gps定位誤差的自回歸（ ar ）模型和自回歸滑動平均和（ arma ）模型，並用神經網路方法確定了ar模型參數。
  9. Autoregressive integrated moving average model

    自回歸積分滑動平均模型
  10. Autoregressive moving average model

    自回歸滑動平均模型
  11. This paper studies a data experiment and identification problem of an actual system, in which the steering gear and the satellite - satellite pointing / tracking system act as the study object, based on system identification technique. the main factors that influence identification results and problems that should be paid attention to are analyzed. base on the analysis, auto - regressive moving average with exogenous input model ( armax ) for steering gear and a three - layer predictive control neural network model are established

    從理論的角度研究了對於一個實際系統的數據實驗設計和模型辨識問題，分析了影響系統辨識結果的主要因素以及在辨識過程中應注意的問題，並以此為依據，建立了舵機的滑動平均模型和星星天線指向控制系統的三層預測控制神經網路模型。
  12. Based on the classical least squares method ( rls ) in system identification, the several new identification algorithms of parameter estimation for the autoregressive moving average ( arma ) model, are presented. they include univariable and multivariable two - stage recursive least squares - recursive extended least squares ( rls - rels ) and two - stage recursive least squares - pseudo - inverse ( rls - pi ) algorithms

    本文在系統辨識經典的最小二乘法（ rls ）的基礎上，提出了自回歸滑動平均（ arma ）模型參數估計的一些新的辨識演算法，它包括單變量和多變量兩段遞推最小二乘?遞推增廣最小二乘（ rls ? rels ）演算法和兩段遞推最小二乘?偽逆（ rls ? pi ）演算法等。
  13. This paper studies the ways to comfotmate the models of portfolio investment combi - nation, and demonstration analysis, divided into three parts. the first part : exordium. mainly introduces the risk of portfolio investment. the second part : brings forward several kinds of investment combination model, including the traditional markowitz model, multiobjective programming and fuzzy programming. the third part : goes along with the demonstration analysis of each kind of model basted on the shanghai stock market, at the same time, appraises the superiority and inferiority with the single - parameter measurement of tangible achievement. before then, most papers discussed the static models, this paper extends the static models to the dynamic models by the means of weighted moving average and bayes estimation

    本文研究了證券投資組合模型的構造方法及其實證分析，分三部分進行：第一部分，緒論，主要介紹證券投資的風險；第二部分，提出幾種投資組合模型，在傳統的馬柯維茨模型及線性規劃的基礎上，本文另外提出多目標規劃的其它解法，並把前人模糊規劃的理論應用到具體的建模中；第三部分，根據我國的滬市行情，對各種模型進行實證分析，並利用實績的單參數度量對各種模型的優劣性進行評價。
  14. Moving average model

    滑動平均模型
  15. Analyzed the change law of chinese textile clothing export by using autoregresse integrated moving average model. forecast the increase tendency of the export of chinese textile clothing in the future

    摘要利用協整自回歸移動平均模型分析了我國紡織品服裝出口變化規律，對未來幾年中國紡織品服裝出口總額變化趨勢進行了預測。
  16. There are a lot of methods, for example, auto - regression model ( ar ) 、 moving average model ( ma ) 、 auto - regression moving average model and data mining 、 higher - order statistics which has been developed in recent years

    時間序列分析方法也是多種多樣，像傳統的自回歸模型、滑動平均模型、自回歸滑動平均模型以及近年來迅速發展的數據挖掘和高階統計量方法等等。
  17. Immunity mix algorithm based on the continuous differential function is proposed in this paper, and its astringency is proved. from here we get the accurate estimator method of the autoregressive moving average model coefficient

    本文首先提出了通用的基於連續可導函數的免疫混合演算法，並證明了其收斂性，由此我們得到了自回歸滑動均值模型系數的精確估計方法。
  18. By using autoregressive integrated moving average model and on the basis of chinese textile and clothing export data from january of 2000 to december of 2004, this paper carries out forecast analysis for the chinese textile clothing export tendency of 2005 and 2006

    摘要本文利用單整自回歸移動平均模型，依據2000年1月至2004年12月中國紡織品服裝出口額數據，對2005年和2006年中國紡織品服裝出口走勢進行預測分析。
  19. Trend prediction and fault diagnosis tech., etc. the information intelligent processing technology facing the application is presented as an emphasis. after introducing the development situation and the whole pattern on related fields, this paper describes several algorithm applied in the simulation experiment, including direct multi - steps nonlinear autoregressive - moving average ( narma ) prediction model based on diagonal recurrent neural networks and fuzzy neural networks model based on generalized probability sum ( gps ) and generalized probability product ( gpp ), and lists the algorithm steps facing the application

    作為重點，本文辟用了較大的篇幅討論面向應用（主要是趨勢預測與故障診斷）的集成智能信息處理技術，在介紹相關領域的發展情況和總體格局之後，重點闡述了幾種基於神經網路的智能演算法，包括基於對角遞歸神經網路（ drnn ）的直接多步非線性自回歸滑動平均（ narma ）預測模型，以及基於廣義概率和（ gps ）與廣義概率積（ gpp ）兩種運算元的模糊神經網路模型，給出了它們的詳細演算法及面向應用的運算步驟。
  20. For the general season time series, according to the model of season autoregressive integrated moving average, the concept of horizontal and lengthways trend are gave, and a new season time series model is brought forward. and then the process of modeling is simplified consumedly. to the estimate problem of a kind of time series, the model performance is good

    對於一般的季節時間序列，我們基於季節自回歸求和均值模型，引入了橫向和縱向趨勢的概念，提出了一類新的季節時間序列模型，大大簡化了建模的過程，對一類時間序列的預測問題，模型性能表現良好。
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