Symbolic Representations of Time Series... SAX is just as good as other representations, or working on the raw data for most problems (Slides shown at the end of this presentation)
標(biāo)簽： Representations representations Symbolic working
上傳時(shí)間： 2014-01-16
上傳用戶：dongqiangqiang
A fourier filter for time-Series signals. Does not require the Signal Processing Toolbox. There is an interactive version with sliders that allow you to adjust the filter parameters continuously while observing the effect on your signal dynamically.
標(biāo)簽： time-Series Processing fourier Toolbox
上傳時(shí)間： 2013-12-19
上傳用戶：change0329
Interactive smoothing for time-Series signals, with sliders that allow you to adjust the smoothing parameters continuously while observing the effect on your signal dynamically. Run SmoothSliderTest to see how it works.
標(biāo)簽： smoothing Interactive time-Series signals
上傳時(shí)間： 2014-12-07
上傳用戶：xinyuzhiqiwuwu
【書名】非線性時(shí)間序列分析 【原 書 名】 Nonlinear Time Series Analysis 【原出版社】 Cambridge University Press 【作 者】Holger Kantz,Thomas Schreiber [同作者作品] 【叢 書 名】 劍橋非線性科學(xué)系列 【出 版 社】 清華大學(xué)出版社 【書 號(hào)】 7302039062 【出版日期】 2001 年3月 【開 本】 16開 【頁(yè) 碼】 304 【版 次】1-3
標(biāo)簽： University Nonlinear Cambridge Analysis
上傳時(shí)間： 2013-12-27
上傳用戶：as275944189
混沌時(shí)間序列預(yù)測(cè)(chaotic time series prediction) RBF神經(jīng)網(wǎng)絡(luò)一步預(yù)測(cè) - \Prediction_RBF\Main_RBF.m RBF神經(jīng)網(wǎng)絡(luò)多步預(yù)測(cè) - \Prediction_RBF\Main_RBF_MultiStepPred.m Volterra級(jí)數(shù)一步預(yù)測(cè) - \Prediction_Volterra\Main_Volterra.m Volterra級(jí)數(shù)多步預(yù)測(cè) - \Prediction_Volterra\Main_Volterra_MultiStepPred.m
標(biāo)簽： Prediction_RBFMain_RBF prediction RBF chaotic
上傳時(shí)間： 2014-08-21
上傳用戶：yangbo69
This program calculates Approximate Entropy of a given time series. Approximate Entropy is a statistic which is used to predict the regularity in agiven time series. Apen( Approximate entropy ) is a recently developed method highly used in biomedical applications.
標(biāo)簽： Approximate Entropy calculates program
上傳時(shí)間： 2013-12-27
上傳用戶：電子世界
A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the DWT and maximal overlap DWT. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation. A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
標(biāo)簽： univariate and bivariate analysis
上傳時(shí)間： 2015-06-23
上傳用戶：chongcongying
工程計(jì)算MATLAB code to calculate the reorthogonalized sine tapers input: N = the length of the time series data to be tapered p = the number of tapers requested I = the gap structure a vector of length N I(t) = 1 if there is data at time t, t=1, ..., N I(t) = 0 if there is a gap at time t output: X = N-by-p vector of the reorthogonalized sine taper
標(biāo)簽： the reorthogonalized calculate MATLAB
上傳時(shí)間： 2013-12-17
上傳用戶：wangyi39
一個(gè)非常好的時(shí)間序列工具箱，詳細(xì)使用說明見P. M. T. Broersen, Automatic Spectral Analysis with Time Series Models, IEEE Transactions on Instrumentation and Measurement, Vol. 51, No. 2, April 2002, pp. 211-216.
上傳時(shí)間： 2014-01-14
上傳用戶：古谷仁美
Abstract：Noise frequency modulation(FM)jamming。which belongs to blanket jamming。is already become the main form ofnoise jamming at present。because the wideband was gained by it．Tne spectnlnl ofnoise FM jamming is analyzed by time domain autocorrelation method in this paper．It’S jamm g peculiarity and幾o(hù)ut— putting signal’S jamming peculiarity ale explained．At last，these time series models ofnoise FM jalllIIling sig— nal and幾o(hù)utputting signal ale built．
標(biāo)簽： jamming modulation frequency Abstract
上傳時(shí)間： 2015-10-17
上傳用戶：lijinchuan
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