time series trend 中文意思是什麼

time series trend 解釋
時間數列趨勢
  • 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;...
  • trend : n (路、河、海岸、山脈等的)走向;方向,方位;傾向,趨勢,動向。 the trend of events 形勢。vi 走...
  1. 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。
  2. Bayesian estimation of time series model with trend

    時間序列趨勢項的貝葉斯估計
  3. Then the paper investigated the regularity of different oil indices using time series statistical analysis method, which suggested that there are some regular components in it, including long - term secular trend, seasonal component and long - term cyclical component. the irregular component also plays an important part in it, mainly including the policy of opec, war, all kinds of international convention for the prevention of pollution from tankers and so on. and then a study of simulation and forecasting performance of arima time series model was conducted to crude oil indices, evidence shows that arima model performs better, especially for short - term forecasting

    在此基礎上,本文以時間序列分析作為基礎研究手段,以德國海運費率指數公布的1980年1月至1999年12月的四類油運費率指數為研究對象,分析了四類油運費率指數的長期變化趨勢、季節變化規律、長期周期循環變化規律和不規則變化規律,並應用arima時間序列模型對160000dwt以上的原油運費率指數進行了短期預測,取得了較好的預測效果。
  4. Considering the characteristic of vibration of rotary machines, this thesis makes a thorough discussion of forecasting the trend of vibration by a means of time series model, puts forward means of processing the nonstationarity, nonnormality and singular value of the field data and distinguishing their models to build a appropriate model and gets precise mulstep forecast to the trend of vibration

    針對旋轉機械的振動的特點,本文深入討論了利用時間序列模型預測振動趨勢的方法,並提出了如何處理現場數據的非平穩性,非正態性,奇異值和模型類型判別方法,以構建合適的模型,實現對振動趨勢進行準確的多步預測。
  5. In chapter 2, an economic concept - location quotients ( lq ) is introduced into the mathematical part of this article, in order to isolate what a city does well, and to find which of its industries export to the rest of the nation. author manipulates last five years " lq from data on farming, forestry, animal husbandry, coal, rude oil, tourism, export and import, population and etc, argues that we could know weather there is a larger than normal concentration of activity in the region, and weather there is a trend of regular develop trace of this activity by running a time series simple autoregression, which provides a feasible analysis tool for people to judge and choose an advantageous industry within this region

    第二章,採用區位商的方式和賦予的經濟意義,通過計算,比較了過去5年中甘肅、寧夏兩省區在農業、林業、畜牧業、漁業、煤炭、原油、旅遊、進出口、人口等與資源產業密切相關的行業的區位商,並提出通過對所獲得的區位商數據建立有序的單變量時間序列回歸模型,可以獲知某項資源產業是否在該省具有明顯的優勢的計量方法,為判斷並選擇區域性的優勢產業提供了一種可行的分析工具。
  6. According to the numbers of segmentations, dts has multi scale feature and can reflect different trend similarity of time series under various analyzing frequency. 2 ) an enhanced algorithm, based on dual threshold value, and the conception of sub - series linear are proposed. relative point average error is used to measure the linear degree of sub series, which produced by bottom _ up algorithm

    對應時間序列線性分段數目的不同,序列趨勢距離具有基於時間的多尺度分析特性,可以有效反應不同分析頻率下時間序列的相似程度; 2 )採用相對點平均殘差衡量bottom _ up演算法劃分的子序列線性度,提齣子序列線性度概念和一種雙誤差閥值改進演算法,大大提高了趨勢序列模型的準確性。
  7. Ann based on trend identify and the application in hydrological time series forecast

    基於趨勢辨識理論的神經網路及其在水文時間序列預報中的應用
  8. Unit root test for seasonal time series with seasonal linear trend

    的時間序列模型的建立與分析
  9. Firstly, utilizing grey - separate model makes time - series take speadily, that is to say, use the grey model to prune the trend one x ( t ) ; and the array got is a steady time array y ( t ) ; secondly, using arima models y ( t ) ; lastly, we get combined predication model of w ( t )

    首先利用灰色分離模型法使時間序列平穩化,即利用灰色模型削去趨勢項x ( t廠得到的序列既是平穩時間序列y ( t廣然後利用aaima模型法對y ( t )建模,最後得到原始數列叫t )的組合預測模型。
  10. This paper analyzed the noniinear, non - - equilibrium, fractai and chaos characteristics of chinese stock market, identified, estimated and tested three fractionaliy integrated time series models the first chapter " introduction to the evoiution of stock market investment theory " summarized the nine important representative theories of different stage, summed up the trend of the development that the stock market investment theory is evotving from static portfplio theory to dynamic time series modei, from univariate modei to muitivariate modei, from linear modei to nonlinear complicated model and from traditional modei to fractai modei, paved the way for following discussion

    實際情況卻是股票市場影響因素以及各因素之間相互作用關系復雜,受投資者個人及群體心理因素影響明顯,股票的波動以及收益與風險的關系常常是非線性的,非均衡的,收益的方差和均值是自相關的、不穩定的,收益的波動符合分形布朗運動,表現出分形和混沌的特徵。本文分析了股票市場的波動的非線性、非均衡、分形和混沌特徵,建立並檢驗了幾種股票的分形差分異方差時間序列模型。
  11. The important research is about the theory and methods of the cluster analysis in view of statistical theory, the theory and methods of fuzzy cluster analysis, the fkn " s structure and the fkn ' s study algorithm ( fkn, fuzzy kohonen network ) - the organic fusion of the fuzzy c - means algorithm and self - organized feature map neural network. the paper proposes the ifkn ( improved fkn ) on the basis of the hard classification idea and the soft classification idea, then carries on the cluster analysis of the artificial synthetic control chart time series through matlab program and tt ? cluster result matches the cluster result of the famous dataengine " s software of the intellectual data analysis and data mining from german mit company. finally, the paper discusses the applying of the cluster analysis to the control process, which can be widely applied to the pattern recognition of the parameter " s changing trend during the control process and the image partition processing, and utilizes the ifkn to recognize the thermotechnical parameter " s changing trend based on the engineering of clinker sintering rotary kiln automatic control system of guizhou " s aluminium factory, through which good effect is obtained

    數據挖掘技術在商業領域中已廣泛使用,然而在工業過程式控制制中的應用卻極少,本文正是在這種背景下,對數據挖掘中的聚類分析方法及其在工業過程式控制制中的應用研究作了償試,重點研究了基於統計理論的聚類分析理論和方法,模糊聚類分析理論和方法及模糊kohonen網路( fkn )的結構與學習演算法,即模糊c ? ?均值演算法與自組織特徵映射神經網路( kohonen網路)的有機融合,並根據硬分類思想及軟分類思想提出了改進的模糊kohonen網路( ifkn ) ,通過matlab編程對人工合成控制時序圖數據集進行聚類分析,其聚類效果與當今廣泛使用的數掘挖掘軟體平臺,德國mit公司著名的dataengine智能數據分析和數掘挖掘軟體的聚類效果相當,最後,論述了聚類分析在控制中的應用,它可以用於過程式控制制中的參數變化趨勢的模式識別及圖象分割處理等具體應用中,並以貴州鋁廠熟料燒結回轉窯自動控制系統為工程背景,利用ifkn識別其熱工參量變化趨勢,取得了較理想的效果。
  12. So the main purpose of this research is use time series, by the means of rs ( remote sensing ) and gis ( geographic information system ), on the groundwork of preciser classification data, to understand the change of different types of vegetation cover in the western arid lands in china, and to forecast future trend of the eco - environment of different areas. 1

    因此本研究的主要目標就是利用長時期的遙感數據,以rs ( remotesensing )和gis ( geographicinformationsystem )為工具,在相對準確分類的基礎上了解1982 2000年期間中國西部乾旱區不同植被覆蓋度的時空分佈、面積變化,並對各區域的生態環境發展趨勢作出一定的預測。
  13. 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。
  14. The concise dynamic model of population since the foundation of our state is done by means of intervention and time series method ; at the same time this model makes forecasting of people ' s development trend in the following several years

    摘要本文利用干預時序模型方法簡明扼要地對我國建國以來的人口發展趨勢建立了動態模型,並預測了未來幾年我國人口發展的趨勢。
  15. By means of analysis and classification of historical time series data of shanghai composite index, this paper creates yield distributions that belong respectively to rising market, dropping market and non - trend market

    對上證綜指的歷史時間序列數據進行分析分類,建立分屬上漲市場、下跌市場、振蕩市場的收益率分佈。
  16. After giving a new gray relational degree and discussing its nature, we apply it into the correlation analysis of time series and draw a conclusion that the gray trend relational degree analysis is a successful method on correlation analysis of time series

    給出了一種新的灰色關聯度,討論了其性質,並將它引入到時間序列的關聯分析中.得到了時間序列關聯分析的灰色方法
  17. The test indicated that there is a weak increasing trend in winter time series, but no apparent trend in other time series

    除了冬季降水有一個弱的上升趨勢,其他序列無明顯趨勢。
  18. This chapter is one of the key parts of the thesis. with reference to economist dunning ' s idc theory, time series models are developed in the first sector to analysis the stage of codi. we discover that china is on the second stage of the idc and shows the trend toward the third stage

    借鑒鄧寧的國際投資發展周期理論,建立了我國對外直接投資的時間序列模型,分析表明:我國的對外投資隨人均gnp的增長呈現穩定增長的趨勢,我國目前處在投資發展周期的第二階段,並呈現向第三階段發展的趨勢。
  19. Abstract : explained the dann ( dynamic artificial neural network ) in terms of b uilding model and predicting of time series, presented for the first tim e a new kind of dann anhn ( artificial neural holonetwork ) for predict ing the coming trend of nonliner dynamic time series, gave its mathem atical model and its topological construction

    文摘:從時間序列建模與預報的角度討論了動態神經網路,首次提出了一種新的實現非線性動態時間序列預報的動態網路結構全息神經網路,給出了其數學模型和拓撲結構,並將其應用到了機械設備振動烈度值的預測上,取得了令人滿意的效果。
  20. Using monthly and daily mean temperature data from 726 stations ' s across china mainland for period of 1951 to 2001, the author established the time series of mean temperature for the region, and analyzed the decadal variation and change trend in temperature and temperature - defined growing season over the past 51 years

    利用中國726個站點1951 - 2001年的逐月和逐日平均氣溫記錄,分析研究了中國1951 - 2001年氣溫和1961 - 2000年溫度生長期的變化及其趨勢。結果表明, 1951 - 2001年期間,中國氣溫上升明顯,增暖過程從80年代開始, 1987年以後,增暖趨勢有了進一步加快的趨勢。
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