1. Moreover, the topics modeling of times series, possibilities of forecasting and statistical control of such models are included.

2. Trend Analysis of Time Series Data.

3. By time series analysis, we build models depicting the cutting tool states, coacervate information from dynamic date and construct feature vectors for discrimination.

4. Time series model can simulate and predict the increase of Quercus variabilis population by the difference, periodic method.

5. Secondly, time series trend analysis models in common use are introduced, whose illative process and applicability are also expatiated.

6. In addition, long term time series had long term dependency which could be identified by Hurst coefficients.

7. Further, through the phase space reconstruction of relating time series and fractal analysis, discussion to nonlinear evolution of the system with varying heating power is delivered.

8. Statistical data analysis, time series analysis, and error estimation will be discussed in the context of each lab.

9. Secondly, it expatiates grey theory and Time series Model theory.

10. Furthermore, general methods and procedure for economic time series forecasting models are proposed based on continuous parameter wavelet networks, which are used in forecast simulation of the tim.

10. try its best to gather and create good sentences.

11. The fuzzy time series forecasting differ from classic time series forecasting is lead in the conception, named membership function which contribute much to figure the method.

12. The forewarning control limits and time series regression judgment model are built up based on the reliability series model in a non-repairable system and Shewhart control charts.

13. The idea of seemingly unrelated autoregression model for time series was developed and applied to forecasts of Chinese inflation and foreign economy.

14. In the analysis of time series, the surrogate data test is often performed in order to investigate nonlinearity in the data.

15. Method Explaining changes of hospital indexes through drawing time series sequences and computing partial correlation coefficients.

16. The multivariable fuzzy time series Heurisistic mode is the easiest method follow.

17. The research investigates the consequences of some specific relaxations of these assumptions that are of practical relevance to economic time series analysis.

18. The research explores the determinants of strike activity using regression analyses of yearly time series data obtained from official statistics.

19. The class of models to be considered provides link between conventional time series and econometric models.

20. The research aims to develop new methods of multivariate time series modelling.

21. In this paper , a research on the outlier mining method for time series data is undertaken.

22. A fuzzy classification system was presented by combining the multidimensional time series fuzzy clustering with rule extraction to evaluate the credit.

23. The time variance law of low degree potential coefficients can be obtained from the time series of the Earth potential coefficients.

24. Researchers home or abroad have made many achievements in one of its branches—failure prediction technology based on time series.

25. The nonparametric method and semi - parametric method were focused in nonlinear time series analysis.

26. This paper discusses various methods of test of nonlinearity of time series. Real-life sea clutter data are tested with the IAAFT method combined with redundancy.

27. The basic idea and some kinds of the common time series models and the development characteristics of time series are explained in detail.

28. Then it makes a study and forecast of the earnings rate of five-year stock investment in U S stock market by time series analysis.

29. Fog frequency shows a pattern of two valleys at the beginning and ending of the 46-year period, and a wave trough in intermediate time series.

30. An average cycle which is about 20 days was discovered in the time series of SSE T-Bond Index.

31. The concise dynamic description of the economic phylogenic from 1952 to 1982 is done by means of time series analysis and all around PCA in this article.

32. Drawing on the idea of edge operator to extract time series edge points, propose a novel piecewise linear representation algorithm that based on temporal edge operator.

33. We utilize the spectral analysis of time series to study the series of capital liquidity from 1999 to 2008, disclosing the information of cyclical fluctuation characteristics of capital liquidity.

34. In this paper , we present a nonparametric approach for checking the residuals of time series models.

35. But "we're presuming on the basis of what we found, if you have some kind of super-duper algorithm and you add our time series, its accuracy will go up, as well."

36. Based on the fractal structure of strange attractor and self-affine property of time series, a method is proposed for predicting chaotic time series.

37. As an important tool of testing time series stationarity, unit root test is always used, and cointegration test is also often implied for judging long equilibrium between nonstationary variables.

38. Applying phase space reconstruction method to divide time series into segments, we have mapped original series into multidimensional data space.

39. An independent sample method for original time series based on the least variance of cross correlation coefficient is introduced in this paper.

40. The composition, control principle and dynamic characteristics of time series pulse combustion control system applied to the annealing furnace in Beitai Ductile Cast Iron Pipe Plant are introduced.

40. is a online sentence dictionary, on which you can find good sentences for a large number of words.

41. The AR-GARCH model accurately fits the daily market turnover time series with very significant estimated parameters.

42. The authors also analyze the errors due to noise in measurement and put forward some measures to improve the accuracy of time series analysis with multiple mean method.

43. This paper, firstly analyzes the affect caused by the uncertainty, then studies the statistics and time series based method for spectrographic oil data processing and wear diagnosis. An example ...

44. In this thesis an economic forecasting system (IEFS) has been designed and implemented based on the models of time series forecasting and methodology of knowledge discovery.

45. The first one is chosen using time series method, and the second is obtained from measurements using the autocovariance function of the stationary stochastic process.

46. With this method, the fractal scaleless band of time-series can be identified and the correlative dimension of time series can be calculated according to the parameters given.

47. Based on PLR (piecewise linear representation) of time series, a new representation method of time-series based on trend turning point is proposed.

48. Moving average method is one of time series forecasting method, if time series have no apparent tendency moving, using moving average method can accurately reflect actual situation.

49. The aim was to compare and classify these literatures and put forward reference for scholars who research development, new techniques and trends of time series data mining.

50. In this paper, we use China's macro-year 1989-2007 time series data to test the consumer behavior assumptions and Chinese people's consumption function model presents in the former paper.

51. The evaluating of the approach. Appraise the advantage and deficiency of the method of random time series , and the questions that should be paid attention to while predicting in the real work.

52. RCS of space object is nonstationary time series, it is difficult to do feature extracting using common time series analysis methods.

53. We believe that this method may more reasonable and be useful in estimating long-memory time series.

54. The aim of this paper is to give a systematic account of asymptotic properties of the sample autocovariance, autocorrelation and partial autocorrelation functions of linear stationary time series.

55. Financial time series is composed of bond profit, foreign exchange rate, stock price, futures price, etc.

56. The multiple - goal decision method with time series was applied to the assessment of surface water quality.

57. Their similarity depends on the structure in the phase space of the time series of original observations fields.

58. The mode set method for time series of vibration signal is discussed in this paper, and its computer analysis program is compiled.

59. A mixed autoregressive moving average(MARMA) model is proposed for modeling nonlinear time series.

60. Another innovation of the paper is bringing the comparability mining technology of time series to the analysis of time series on stock market.

61. Aiming at the abnormity of the phase and groove number, hardware redundancy and difference method is used to forecast time series and the problems are resolved.

62. A time series factor analysis was conducted utilizing the history data, from two points of view: the demand of health service and the the providing capacity of health fund.

63. For example, the International Financial Statistics has a header record which describes a time series and the header record is followed by a number of records which contain the actual time series.

64. Time series forecasting models of total food-grain consumption in China were selected by using SPSS, and were built based on the correlative relationship between quantity of consumption and time.

65. This paper proved that a frequency stochastic sequence obtained from a stationarily correlated time series by using discrete Fourier transform is orthogonal.

66. In this thesis we propose three new data flow models: a time series communication model, a processor allocation model and a new data flow architecture.

67. First presented is a method of regression analysis combined with random time series technique for analysis of monitoring data of dams.

68. Use a method of time series piecewise linear representation based on feature points as a way for pattern representation.

69. Making use of time series model and other econometric approach, this article partitions the trend of China's GDP data, and repartitions China's economic cycle on the grounds of the empirical results.

70. A time series forecasting model, based on hierarchical genetic algorithm and BP neural network,[] was proposed.

71. De-noise filter method based on single value decomposition is applied to preprocess traffic volume time series, and a radical basic function neural network is trained for prediction.

72. In order to derive a relatively noise-free time series from the GPS results, an integrated method based on adaptive noise cancelling principles and wavelet de-noise method is presented.

73. Furthermore we assume that each observation of the time series has the same expectation function, standard deviation, and probability distribution function.

74. The limitlessness, mobility, and irregularity of time series data stream make the traditional frequent-pattern mining algorithms difficult to extend to the mining problem of time series data stream.

75. The support vector machine (SVM) was combined with genetic algorithm (GA) to establish a model for predicting short-term chlorophyll a concentration nonlinear time series.

76. The periodic mean superposition method can predict the result through decomposing the hydrological time series into several periodic waves, extrapolating the periodic waves, and linear superposition.

77. The article introduces a temporal association rule model based on time series data. According to the definition, two algorithms are developed and compared.

78. At the same time, the time series applying on model analysis is discussed.

79. The linear interpolation and bilinear interpolation were employed to obtain the values of the proper modes on locations where the wind pressure time series are to be predicted.

80. As one of the branches of statistics, time series analysis focuses on the variation characters and trend of discrete ordered data series mainly.

81. We do the initial identification and the theoretical analysis with the autocorrelation function towards the time series of HSI daily closing quotation.

82. Then, within-plot time series error autocorrelation of basal area data and cutting intensity which were expressed with dummy variable were taken into account in mixed model.

83. In this paper, wind speed time series simulation methods and AIC rule is elaborate.

84. Currently, in the actual production, MRP system is calculated on a time span through sub-total demand and net demand, and to express the demand for materials with the intermittent time series.

85. In this paper we research mainly the ergodic property of the output time series, and we obtain the ergodic state of the output time series in the market economy conditions.

86. The identification method for the one dimension time series, open loop and closed loop system are developed by use of model ARMA.

87. Some results showed as follows. (1) It was found that the time series of outbreak of the locust in Henan Province was stochastic sequence using the way of time series analysis.

88. Many of the core concepts from simple linear regression, for example, establish a good foundation for understanding Multiple Regression, Factor Analysis, Time Series, and so on.

89. According to the phase space reconstruction of a single variate time series, a new phase space delay reconstruction method based on multivariate time series of complex systems is proposed.

90. In the paper, we construct a new seasonal adjustment method of time series on the basis of the structural time series model.

91. Priestley, M. B , Spectral Analysis and Time Series, Academic Press, New York, 1981.

92. A feature extraction method of singular event based on multi-scale wavelet is presented. Time series is divided into event subsequences with independent trend.

93. This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting.

94. In this paper a variety of time series nonlinear models are introduced and the idea and modeling method of the threshold autoregression are discussed in detail.

95. Similarity - based representation and pattern search of time series have vast foreground of application.

96. Numerical test results show that SVR has good ability of modeling nonstationary financial time series and good generalization under small data set available.

97. The outlier pattern can be efficiently detected from time series by this algorithm.

98. An AR model is used to approximate the linear part of the time series; the nonlinear part and the approximate error are regarded as the unknown-input of system.

99. Based on the relative theories of biomathematics , a new approach to trend extrapolation of time series of ecological footprints is proposed in this paper.

100. At the same time, it set up the Forecast Model by the Analysis of Time Series in water consumption both by day and by hour.

101. This article is designed to fit the linear time series model of enterprise annuity index yield to predict yield, to estimate the bounds of loss probability.

102. Time series of horizontal kinetic energy show an obvious seasonal variation with the maximum in winter when jet is strongest and the mimimum in summer when there doesn't exist the jet.

103. Residual control chart is the method of using the residuals of time series models to control the quality of the auto-correlated process.

104. After that, we study the correlations between extreme events using return interval, and compare the multifractal properties of the three types of time series.

105. It must need nonlinear method to reflect essence of the time series veritably.

106. We get the complexity time series from time scan spectrum of hemocyte suspension.

107. Aim To propose a new mode of time series based on the traditionally seasonal time-series analysis.

108. However, the study of the grey system theory mainly bases on the cumulative time series, which emphasizes the new data optimization and researches on real regulation.

109. Aiming at the problem of multivariate time series similarity search, this paper presents the definition of parameter importance degree and puts forward a candidate sets obtaining method based on it.

110. A new methodology based on state space reconstruction and divergence calculation techniques has been developed for financial time series forecasting.

111. In order to simulate narrowband reverberation time series easily, the simulation method designed based on simulating reverberation complex envelop, which is feasible for any sampling frequency.

112. Evolution of rock joints into transfixion is described by the time series from strain-stress curves, and the state of joint system in sandstone is chaotic before its ultimate state.

113. This shows that the HEMA has great potential to provide a new and powerful tool for the analysis of time series.

114. A concept of mean Mahalanobis distance between time series and its simulation result is presented. Based on the concept, a distance test method for dynamic simulation results is established.

115. Unvaried - Time Series analysis and forecasting is an important portion of current signal process and economics.

116. The kernel function can be unit hydrograph of deterministic system or time series model of stochastic system.

117. Forecasting model of gas concentration was built using time series and adaptive neuro-fuzzy inference system ( ANFIS ).

118. This paper, presents a nonlinear time series forecasting model based on recurrent composite BP networks, It also establishes a multivariable time series model is aimed at concrete examples.

119. RRDtool is an open source, high performance data logging and graphing system for time series data.

120. The results confirm the feasibility of the algorithm in the analysis of hydrological time series.

121. Based on infrared communication technology, the infrared coding module NB9148 is used in the transmitter of local controller. The receiving terminal SJ1838 is used to decode the infrared time series.

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