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The time series model is decomposed into the trend items, cycle items and random items, respectively extracted by the establishment of the various forecasting model, the model is applied to the Chahayang farm in 1956 to 2008 on-year growth period crops fitting rainfall forecast, the results show that the model can reveal the crop growth period variation of monthly rainfall, for the rational development...
Bass model have been used widely for diffusion curve of a new product in some countries of the world, but only a few studies have explored in the parameters' estimation of bass model. In this paper, Bass model is adopted to fit the diffusion curve of mobile subscribers of China with different parameter-estimation techniques respectively, such as NLS(Non-Linear Least Squares ) ,OLS(Ordinary Least Squares),...
Wind can be considered as an interesting alternative to fossil fuels, but as a power source, is both intermittent and diffuse. It is necessary to calculate wind equivalent capacities in order to introduce a coherent evaluation of wind production in the management of the centralized production park. Base on the sequential Monte Carlo simulation technique for wind speeds, an auto-regressive and moving...
Selecting gross domestic product(GDP) and gross output from 1990 to 2007 of Henan Province as time series, this paper constructs triple exponential smoothing models to predict GDP and gross output in 2010 and 2012 of Henan Province, and then draws up the price input-output tables in 2010 and 2012 for Henan Province by means of prorate distribution and the RAS approach according to the predicted values...
In this paper, we build an electric vehicle's time-series model of patent technology based on the analysis patent data, conducting to the status and trends of electric vehicle's patent applications; based on the patent applications in the related fields, time and the applicant and the International Patent Classification corresponding analysis, we research and predict the priority areas of electric...
Based on autoregressive distributed lag model, this study examines the long-run relationship between carbon intensity and energy efficiency, income level, coal price and industrial composition in the case of China by employing time series data of 1980-2007. The results show that there is a long-run equilibrium relationship and short-run adjusting relationship between China's carbon intensity and Per...
The conventional prediction model of GPS static point positioning (GSPP) system is usually considered that the positioning errors are caused by the external stochastic factors. And many independent models were built for every single type of errors. Actually, the observed error time series is mostly a seemingly random nonlinear chaotic series. In this paper, phase space reconstruction and chaotic characteristic...
This paper studies the Shanghai Stock Exchange (SSE) Composite Index, sample period of which spreads from December 16th 1996 to December 31st 2009. The index close prices, its logarithm, its logarithmic first differences, and its log linear detrended series are used. To judge the existence of chaotic dynamical features in time series, the technique of phase space reconstruction is applied. The C-C...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
A novel method to diagnose the bearing fault is presented. The proposed method is based on the analysis of the bearing vibration signals using Singular Spectrum Analysis (SSA). SSA is a non-parametric technique of time series analysis that decomposes the acquired bearing vibration signals into an additive set of time series to extract information correlated with the condition of the bearing. Information...
In the field of geoscience and atmospheric science, raw data should be interpolated by appropriate times due to the temporal and spatial resolution limitation or the length of initial data at the given observation time for the follow-up process. But this type of data have universal and special nonlinear characteristics, such as chaotic and fractal feature, these nonlinear time series are sensitive...
Nonlinear co-integration method is discussed. According to the generalized fractal co-integration relationship, one form of nonlinear co-integration is proposed based on GPH (Geweke, Porter-Hudak) method. Using the tests of the long-memory characteristic in finance time series, based on daily price series of RMB exchange rate and Shanghai stock markets from July 22, 2005 to April 24, 2009, empirical...
An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of PRBF is a weighted...
For improving the reliability of the data stored up, this paper brings forward an improvement scheme. It expands the error correction from One-dimensional (1-D) model to Three-dimensional (3-D) model. This thesis has listed and has analyzed three models' (1-D, Two-dimensional (2-D) and 3-D) structure method and their error correction process, and from all-round aspects to compare the three kinds of...
Aim of the intrusion detection is to availably detect the intrusion from large stores of data. IDS (Intrusion Detection System) analyses sorts of data resource with data mining techniques to extract useful patterns and rules that could be used to guide the IDS to analyse intrusion. Allowing for characteristics of intrusion detection, a novel approach of mining frequent serial episodes from streams...
This paper presents a comparison of data mining techniques for wind power forecasting in a time frame out to 15 minutes ahead. The forecasting is focused on the power generated by the wind farms and the power changes are predicted by using multivariate time series models ARMA, focus time-delay neural network (FTDNN) and a phenomenological model of the turbines. All these models are tested with real...
In Service Oriented Architecture, Quality of Service (Qos) represents an important issue which is often considered when selecting and composing services. For receiving up-to-date information, non-functional properties can be continuously monitored using current methods. Because of the occurrence of monitoring at every time of service calling, the current methods imposes some overhead on the SOA. The...
Based on a formal characterization of time-series and state-sequences, a new distance measurement dealing with both non-temporal and temporal distances for state-sequence matching is proposed in this paper. In addition to formulating the temporal order over state-sequences, it also takes into account of temporal distances in terms of both the temporal duration of each state and the temporal gaps between...
The aim of this paper is to solve the blind source separation (BSS) problem using the temporal independent component analysis (ICA) model. In contrast to ordinary ICA, except for independent assumption, the temporal structure of the source components is taken into account. After combing the virtues of both high order statistics and the temporal second-order information of the source signals, we can...
The purpose of this paper was to model the daily stem water content with neural network. The output voltage of stem water content sensor changed as time series. In order to ensure the accuracy of the model, coefficients sc and eg must be adjusted with the RBF NN input vector changed. The dimensions of input vectors were grouped from 2, 4, 5 separately. After being grouped, observed data were input...
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