prediction of time series 中文意思是什麼

prediction of time series 解釋
時間序列預測
  • prediction : n. 1. 預言，預告。2. 被預言的事物。3. 【氣象學】預測，預報。
  • of : OF ＝Old French 古法語。
  • 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. 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
  2. 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。
  3. 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。
  4. In rsdm, binary patterns are replaced by real - valued patterns, accordingly avoiding the coding process ; the outer learning rule is replaced by regression rule, therefore the model has not only the ability of pattern recognition but the ability of function approximation. the prearrangement of the address array bases on the distribution of patterns. if the distribution of patterns is uniform. then the address array is prearranged randomly, otherwise predisposed with the theory of genetic algorithm and the pruneing measure so as to indicate the distribution of patterns and improve the network performance. non - linear function approximation, time - series prediction and handwritten numeral recognition show that the modified model is effective and feasible

    在rsdm中，以實值模式代替二值模式，避免了實值到二值的編碼過程：以回歸學習規則代替外積法，使該模型在具有識別能力的同時具有了對函數的逼近能力；地址矩陣的預置根據樣本的分佈採取不同方法，若樣本均勻分佈，則隨機預置，否則利用遺傳演算法的原理和消減措施來預置地址矩陣，使之反映樣本的分佈，改善網路的性能。
  5. In this paper we adopt this new chain to construct chained dls - icbp network and greatly boost the performance of multi - steps time series prediction

    實驗證實基於dls的新型鏈結構網路較傳統的dls - icbp和icbp鏈結構網路的多步預測性能有較大提高。
  6. Time series analysis applied in prediction of rmb ' s exchange rate

    時間序列分析方法及人民幣匯率預測的應用研究
  7. 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
  8. Finally, bp neural network recognition model of particulate and aggregative fluidization and rbf neural network prediction model for chaotic time series of circulating fluidized bed have been set up, which provides new methods for on - line recognition of fluidization state and control and prediction of circulating fluidized bed systems

    最後建立了散式流化和聚式流化bp神經網路識別模型和循環流化床中的混沌時間序列的rbf神經網路預測模型，為流型在線識別和循環流化床系統的控制和預測等提供了新的方法。
  9. In this article, an equivalent definition of reconstruct function is drew in the state space reconstruct by time delay chaotic time series, that lead the prediction more conveniently. then introduce a weighted distance to depict neighbour points of prediction which insured the similarity of the neighbour points

    本文在時間序列坐標延遲后重構的相間中，作出重構函數的等價定義，提高了預測的可操作性；並在此基礎上，引入了加權距離來刻畫預測向量的鄰近點，保證了鄰近點的相似性。
  10. Time series prediction for deformation of surrounding rocks in soft rock bolt - grouting roadway

    軟巖錨注巷道圍巖變形量的時序預測
  11. Abstract : a simple scheme of establishing bilinear time series model ( bm ) is presented for predicting atmospheric co2 concentration. the example shows that the scheme is practical and universal, which has major theoretic value and wide - ranging application in prediction of various nonlinear time series

    文摘:提出了用雙線性模型預測大氣co2濃度序列的一套簡便方案.實例計算結果表明，該方案具有實用性和通用性，在各種非線性時序預測中具有重要的理論意義和廣泛的應用價值
  12. The theory of chaos and fractal have are widely applied on economics and finance field since the 70 ' s last century. talking about our country ' s studies on this way, as whole, these studies as followed have been doing, recognizing of system chaos, looking for chaos attractors, researching fractal structure to time series curve, prediction and control to chaos system etc. all those studies need deal with the estimation of the fractal - dimension

    分形與混沌作為非線性科學中兩個重要組成部分，從上世紀七十年代起在經濟、金融研究中得到廣泛應用，就目前我國在這個領域的研究現狀看，其應用研究主要集中在系統的混沌識別，混沌吸引子是否存在，時間序列曲線分形結構的分析，混沌系統短期預測與控制等問題上。
  13. Improvement of time - series method for short - term traffic prediction

    短時段交通預測時間序列方法的改進
  14. In order to overcome the present numerous methods for water prediction and the complex model, which make the actual selection of prediction methods more difficult. according to the changing characteristic of water and analysis theory of auto correlated formation, the data formation of time series was discerned, optimal selection methods for the model of water prediction were raised

    為克服目前用水量預測方法眾多，模型繁雜而給實際預測方法選擇帶來困難的情況，根據城市用水量的變化特徵，通過自相關分析理論，對時間序列的數據模式進行識別，提出了用水量預測模型的優選方法。
  15. Applications of time series smoothing method in the prediction of product output

    時間序列平滑法在產品產量預測中的應用
  16. According to the takens embedding theorem, the nonlinear time series is converted into discrete dynamic system. the prediction of time series is achieved by the observation of system states

    將由系統的輸入輸出變量組成的非線性時間序列通過空間嵌入的方法轉化為一個離散動態系統，通過對系統狀態的觀測實現時間序列的預測。
  17. Based on the statere configuration system, an unknown input observer is presented for the prediction of time series. the approximate error of the ar model is regarded as the unknown - input of system

    以實時擬合時間序列的線性ar模型作為時變系統的已知線性部分，將擬合誤差作為時變系統的未知輸入，實現了系統狀態的多步預測。
  18. The bic method generalized from ar model was adopted to determine the number of input neurons in grnn prediction model. the grnn was applied to single - step and multi - step ahead prediction of the vibration time series of a rotating machine, and its performance was compared with that of 3 - layers perceptrons network with error back propagation training algorithm ( bpnn ). it is indicated that the grnn is more appropriate for prediction of time series than the bpnn, and the performance of grnn is qualified even with sparse sample data

    研究了基於廣義回歸神經網路（ grnn ）的大型旋轉機械振動狀態預測，提出了應用bic準則確定grnn預測模型輸入神經元數目的方法，將grnn用於大型機組振動峰?峰值時間序列的預測，與採用誤差反向傳播學習演算法的三層前饋感知器網路（ bpnn ）的預測結果對比表明， grnn的預測性能優于bpnn ，而且，即使樣本數據稀少，也能獲得滿意的預測結果。
  19. The commonly used quantitative forecasting method mainly includes the model of time series and cause - effect model, which need to set up corresponding mathematics model according to the historical materials and to makes prediction of the development trend of the logarithm row

    常用的定量預測方法主要包括時間序列模型和因果模型。這些模型都是根據歷史資料建立相應的數學模型，對數列的發展趨勢做出預測。
  20. In this essay, firstly the author analyzes the predictability of time series from china ' s stock exchange using three kinds of methods : arma model, neural network model and non - parametric estimation and gives evaluation on their performances while at the same time puts forward some conclusions deserving attention from both stock exchange supervising department and stock traders. secondly, the author examines the assumptions closely on which the above - said methods base and gives a detailed discussion on them, especially using garch model to test quantitatively the stability of china ' s stock exchange, afterwards drawing the conclusion that it is hard to make accurate prediction of price or return rate of china ' s stocks for none of the assumptions fully holds ground. thirdly, taking account of the difference between chinese stock traders as a whole and that of developed countries, the author gives a thorough analysis on the complexity and volatility of its ( traders " ) reaction to information and points out that the intrinsic heterogeneous and volatile reaction to information is an important reason for the almost unpredictability of the price or return rate in china ' s stock exchange

    本文首先採用arma模型、非參數模型以及神經網路模型對我國股市時間序列進行研究，對三種方法在分析我國股市時間序列的表現進行評價，並得出了一些對監管部門以及股票交易者有借鑒意義的結論；其次作者對三種模型分析我國股市時間序列的前提進行了討論，特別是利用garch模型對我國股市的系統穩定性進行了量化檢驗，得出了前提難以滿足導致準確預測我國股市價格或收益率困難的結論；第三，考慮到中國股市股票交易者群體與發達國家股市股票交易者群體之間的差異，作者借用行為金融學的理論成果對我國股票交易者對信息反應的復雜性和易變性進行了詳細分析，指出股票交易者對信息反應的異質性和易變性是造成難以準確預測我國股市的一個重要原因，考慮到我國股市以散戶為主導的特性將長期存在，因此將行為金融學的研究結論納入對我國股市時間序列的量化研究具有重要的意義；最後，作者從唯理預測與唯象預測之間差異的角度出發，指出了唯象預測的缺點並對我國股市時間序列的研究方向進行了展望。
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