時間數列 的英文怎麼說

中文拼音 [shíjiānshǔliè]
時間數列 英文
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...
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 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...
  • 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...
  • 數列 : progression; series; a series of numbers arranged according to a certain rule
  1. Agglomerative effectiveness : the effect on regional economical inequality because of industrial agglomeration. in order to show the effects of industry on regional inequality, the model of panel data is applied to analyze the relationship between industrialization and economy growth., which is helpful to estimate whether the tendency of growth is convergence and the structural effectiveness. the time series model is used to analyze the effect of industrial agglomeration on regional inequality, where gini coefficient is taken as the index of industrial agglomeration

    為了更清楚地把握工業在地區差距上的效應，本文用面板數據模型分析工業化程度和經濟增長之間的相關關系，從而判斷區域經濟發展趨勢是否收斂，工業在「結構效應」方面的影響;計算表示工業集聚程度的基尼系數，通過時間序列模型分析工業集聚對地區經濟差距的「集聚效應」影響。
  2. In this article i do a lot of analysis for the data formed in the mobile samples with the basis of the research of data mining, mainly including : it analyses and summarizes the theory and technology of data, especially the further discussion of the data mining algorithm for time sequential. it introduces the course of the test curve of the power transmission system of electric mobile and discusses the technology and methods of pretreatment for curve data. it studies and develops the antitype system for the analyses of test curve data of the power transmission system of electric mobile with the mining and analysis of test curve data of the power transmission system of electric mobile and the basis of the algorithm of time sequential

    本文以目前數據挖掘的研究為基礎，對汽車樣品試驗中形成的大量數據進行分析處理，主要研究內容包括:分析和綜述了數據挖掘理論基礎和相關技術，特別是對時間序列挖掘演算法進行了深入的討論介紹了電動汽車動力傳動系統檢測曲線生成的基本過程，討論了曲線數據的預處理技術與方法以時間序列挖掘演算法為基礎，對電動汽車動力傳動系統檢測曲線數據進行了挖掘與分析，研究並開發了電動汽車動力傳動系統檢測曲線數據分析原型系統。
  3. We describe the meaning of chaos > future idea of chaotic theory and influence on forecast ; introduce the character of chaotic time series, and point out the problem and shortage of the methods already existed computing character value which are fractal dimension and the largest lyapunov exponent and improve on it ; present the forecast principle of forecast method based on chaotic attractor, and point out the shortage of local field forecast method based on chaotic attractor and bring forward improved on methodo at the same time, we put forward a banausic algorithm and compare two models using practical example

    論述了混飩的含義與混淪理論的未來觀及其對預測的影響；介紹了。混飩時間序列的特徵，指出了己有的計算分形維及最大李雅譜諾夫指數這兩個特徵量的方法存在的問題與不足，並對此進行了改進；給出了基於混飩吸引子的預測方法的預測原理，指出了常用的基於混燉吸引子預測的局域法的不足並給出了改進方法，同時，給出了其實用演算法，並用實例進行了比較。
  4. ( 2 ) it explains the basic concept of time series, some kinds of the common time series models and the development characteristics of time series in detail. it analyses how to judge the model from the self - related function and the deviation related function. determining a better standard to set up models from the comparison of some kinds of fixed step time series standards, then predicts utilizing the counter function

    （ 2 ）詳細闡明了時間序列的基本思想、幾種常見的時間序列模型以及時間序列的動態特徵，分析了如何利用自相關函數和偏相關函數來對模型進行判定，通過對時間序列的幾種定階準則的比較，確定一種好的定階準則來建立模型，從而可以利用逆函數法進行預報。
  5. We ca n ' t divide the multiple streams time series into singleness times series simply in the research of multiple streams time series, we ' ll dissever the relation between the events of the multiple streams. although the msdd can find the dependency relationship of multiple streams, but it have n ' t the initialization of the events, the express of the time relationship between events is not frank, the cost of the algorithm is expensive ( o ( n5 ) ), i ca n ' t find much more knowledge in multiple time series, it find the dependency patterns only of the multiple time series, so there need a new more effective, frank, complete algorithm to find the knowledge

    研究多流時序不能簡單地將它割裂為單流時序，因為這樣就割裂了數據流事件之間的關系。雖然msdd能夠發現多流時間序列中的依賴模式，但是由於其缺少對數據的初始化、事件之間時間關系的表示不直觀、演算法執行的時間空間開銷很大（ o （ n ~ 5 ） ） 、不能夠充分發現多流時間序列包含的知識，它只發現依賴關系，因此研究新的，高效，全面的發現多流時間序列事件之間關系的演算法成為必要。本文分析了單一和多流時間序列中的知識發現，把多流時間序列事件內部存在的關系表示為：關聯模式、依賴模式、突變模式。
  6. In this thesis, flow regime identification and malfunction diagnosis of fluidization were studied with fractal technology, the presented methods for both flow regime identification and malfunction diagnosis were proved of effectivity. main achievements are as following : 1. fractal brownian motion ( fbm ) was made from gauss noise and compared with pressure fluctuation signal of gas - solid fluidization, which demonstrated the similarity between the fbm and the signal

    在通過fbm （分數布朗運動）數據模擬證明了氣固流化床壓力波動信號與分數布朗運動是相似的基礎上，提出了用分數布朗運動來模擬氣固流化床壓力波動信號，並採用r s分析法從信號時間序列中提取出hurst指數，通過分析信號hurst指數值對流化床流型和結塊故障進行了研究。
  7. Data processing and time series analysis for gps fiducial stations in china

    跟蹤站數據處理與時間序列特徵分析
  8. This is called a deseasonalizing time series.

    這叫調和時間數列。
  9. Since the data are available on a quarterly basis we must first deseasonalize the time series.

    由於數據隨季節而變化，所以必須對時間數列進行調整。
  10. We use the term time series to refer to any group of statistical information accumulated at regular intervals.

    按照相等的時間間隔收集起來的一組統計數據叫時間數列。
  11. " for methods of analyzing economic time series with common trends cointegration

    .以表彰他們在「分析經濟時間數列」研究領域所作出的突出貢獻
  12. The empirical result of analyzing here can be concluded as follows : the error of adaptive neuro - fuzzy inference systems ( anfis ) is the smallest, multivariable fuzzy time series models is the second smallest, and grey forecasting is the third smallest

    實證結果得知在有限資料筆數下，適應性類神經模糊推論系統（簡稱anfis ）預測結果較佳，多變量模糊時間數列模式次之，灰色預測模式第三。
  13. During this course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time - series analysis and prediction

    課程期間我們將檢視數種學習技巧在一些領域上的應用如電腦視覺、電腦繪圖、數據庫搜索和時間數列分析與預測。
  14. Correlation of time series

    時間數列相關
  15. Revise the process of model construction, compares forecasting results by different conditions. ( 3 )

    （ 3 ）比較三種多變量模糊時間數列模型在不同等份下。
  16. Furthermore, it also demonstrates that stock price and time frame do not appear to have linear relation

    並使用時間數列分析來看減資首次宣告、私募首次宣告及減資暨私募首次宣告，對股價波動的影響。
  17. The multivariable fuzzy time series heuristic model is the easiest method to follow

    而在方法操作上，以多變量模糊時間數列引導式模式最?簡易。
  18. Chapter2 : traditional time series models and multivariate fuzzy time series models. the chapter introduces the vector arma model, transfer arima model, seasonal arima, and arima model of traditional time series models, and two - factors models, heuristic models, and markov models of multivariate fuzzy time series models. i devise the process of the model construction, and propose the findings

    本章介紹傳統時間數列模型（向量arma模型、 arima轉移函數模型、季節性arima模型以及arima模型）與多變量模糊時間數列三種模型?二因子模型（ two - factormodels ） 、引導式模型（ heuristicmodels ） 、馬可夫模型（ markovmodels ） ，模型建構步驟與流程，及傳統時間數列模型轉換為多變量模糊時間數列模型過程，並分別針對多變量模糊時間數列三種模型提出本研究不同於先前研究之處。
  19. This thesis explored the application of the forecasting methods of arima time series and multivariate fuzzy time series : two - factors models, proposed by chen and hwang ( 2000 ), heuristic models, proposed by huamg ( 2001 ), and markov models, proposed by wu et. al. ( 2003 ). this thesis employed five to sixteen intervals to instead of the method proposed by huarng ( 2001 )

    本文的研究重點在探究近期理論界提出的三種多變量模糊時間數列模型? ? chen和hwang （ 2000 ）所提出的二因子模型、 huarng （ 2001 ）所提出的引導式模型、 wu等（ 2003 ）所提的馬可夫模型，分別針對各模型的建構步驟、適用場合，及上述文獻未達到的部份，再做深入研究，並比較其結果。
  20. The multivariable fuzzy time series heurisistic mode is the easiest method follow

    而在方法操作上，以多變量模糊時間數列引導式模式最為簡易。
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