time series 中文意思是什麼

time series 解釋
第五節時間序列
  • time : n 1 時，時間，時日，歲月。2 時候，時刻；期間；時節，季節；〈常pl 〉時期，年代，時代； 〈the time ...
  • series : n 〈sing pl 〉1 連續；系列。2 套；輯；叢刊；叢書。3 【生物學】區；族。4 【植物；植物學】輪；列；...
  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. Based on the characteristic of fractured signal, time series analysis can detect the distribution of fractures. because of excellent antinoise ability, in high - order statistics theory, the theory of time series analysis includes more information and resolves more problems than second - order statistics

    時間序列分析法具有很好的抗噪能力，主要採用了高階統計量的方法，它比以前廣泛應用的二階統計量的方法包含了更多的信息。
  3. By two ways, this paper debates the theory of fracture detection : on one hand by the way of edge detection in image processing ; on the other hand by time series analysis. the detection by time series analysis is more antinoise than edge detection in image processing. edge detection theory in image processing mainly includes correlation data, fuzzy edge detection, entropy operator edge detection and gradient edge detection

    圖像處理中的邊緣檢測的方法主要包括相干數據體法、模糊邊緣檢測法、基於熵運算元的邊緣檢測法、梯度邊緣檢測法；其中模糊邊緣檢測法比較依賴于參數的選擇，其渡越點兩邊的像素區別明顯；熵運算元的檢測方法則是檢測的圖像邊緣比較光滑，連通性好；梯度檢測法可以使用不同的運算元核，演算法比較簡單；相干數據體對于總體的大的裂縫的分佈具有比較奸的反應。
  4. 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。
  5. Foundation of black body furnace temperature time series prediction model based on bpnn

    神經網路的黑體爐溫度時序預測模型的建立
  6. Data mining, which has been considered as a important methods in the analysis of time series, received more attention came from boffin. data mining is a process which get the useful information from the vast 、 incomplete 、 noised 、 fuzzy and random data

    數據挖掘技術是從大量的?不完全的、有噪聲的?模糊的、隨機的數據中，提取隱含在其中的?人們事先不知道的?但又是潛在的有用信息過程。
  7. Quantitative tools for the examination of paleoceanographic data will be introduced ( statistics, factor analysis, time series analysis, simple climatology )

    將介紹分析古海洋資料的定量工具（統計、因素分析、時間序列分析、簡易氣候學） 。
  8. ( 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。
  9. An optimized algorithm for mining association rules in hydrological time series is proposed on the foundation of the analysis of variance ( anova ), contingency table test and the new definition of interestingness

    摘要基於方差分析、列聯表檢驗以及興趣度的定義，提出一種挖掘水文時間序列關聯規則優化演算法。
  10. 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。
  11. 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
  12. Finally, the effectiveness and practicability of this method is demonstrated by the simulation results of the famous box - jenkins gas furnace data and mackey - glass chaotic time series

    與以往的模糊聚類辨識方法相比，所需cpu時間大大縮短，具有較高的辨識精度。
  13. Analysis of time series water level data of zhuzhou hydrometric station

    連續動力系統時間序列的非線性檢驗
  14. Thus this paper puts forward the dynamic time series period analysis and prediction model. it combines the basic principle of the stepwise regression period analysis to the multiplayer - transfer method. it can not only effectively select every latent period of a time series, but also take advantage of the selected latent periods to make a long - term prediction

    因此本文提出了動態時間序列周期分析預測模型，它是將多層遞階方法與逐步回歸周期分析的基本原理相結合，使之既可以有效地選取時間序列的各個隱含周期，也可以利用所選取的隱含周期作較長的時間預測。
  15. 24 - hour time series of mean sea level pressure

    平均海平面氣壓的二十四小時時間序列
  16. A result on asymptotic normality for time series sum

    時間序列和漸近正態性的一個結果
  17. The work of the paper mainly includes : ( 1 ) present a model for measuring the similarity between two hydrological time series. in this model, we adopt an intuitive dimensionality reduction technique for hydrological time series which is called piecewise average approximation ( paa )

    主要工作包括： （ 1 ）提出了適合水文時間序列數據特點的相似性模型，採用簡單直觀的等時間間隔序列分段平均值技術（ paa ）作為水文時間序列降維方法。
  18. In addition, the ao index released by american climate prediction center ( cpc aoi ) fails to reflect the summer ao mode. in this paper, the time series of the leading principal component of the summertime ( june - september ) surface level pressure anomaly field over the domain poleward of 20 n is defined as the summertime ao index

    此外，美國氣候預測中心發布的全年北極濤動月指數不能表現夏季北極濤動型，本文將北半球熱帶外地區（ 20 n以北）夏季（ 6 - 9月）海平面氣壓場eof主模態的時間序列定義為夏季北極濤動指數。
  19. Priestley, m. b, spectral analysis and time series, academic press, new york, 1981

    胡基福， 《氣象統計原理與方法》 ，青島海洋大學出版社，青島， 1996 。
  20. The detailed works are as follows : the finding patterns problems in the time - series data sequence are described, and a new trend logic expression method is introduced, and its algorithm and experiment result of algorithm are given ; time - scries data are disposed, and using the arctg. slope of line as the sample of pattern recognition, so ignoring the aberrance of pattern in the classified. in addition, a new time - series pattern finding algorithm based on higher - order neural network is put forward

    同時給出了本文的具體的工作，主要是：對在時序數據序列中發現模式問題進行了描述，並介紹了一種新的趨勢邏輯表示方法，給出了其演算法及演算法的實驗結果；對時序數據進行處理，提出了利用線段的斜率反正切值作為模式識別的樣本，從而在分類時忽略模式的畸變；另外，還提出了一個新的基於高階神經網路的時序模式發現演算法。
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