Changes in stock price will be influenced by many aspects of factors. When we are predicting stock price, it is difficult to build a determined mathematical model between stock prices and these complex factors. This paper first utilizes ε − SVM (ε − support vector machine) to build a stock price prediction model. By fitting the prediction error sequence, we find the law factors, which the predi...

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load. Modeling online and filtering real-time can effectively improve measurement accuracy. Firstly, pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load. Then, set order for the time series model by Akaike informati...

The purpose of this paper is to introduce a method of estimating parameters in nonnegative ARMA processes. The method is a generalization of the procedures which were derived for autoregressive and moving-average processes. The estimates are constructed in the form of minima of certain fractions or some functions of these minima. A theorem concerning the strong consistence of these estimates is...

Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve th...

Model selection is a critical aspect of subset autoregressive moving-average (ARMA) modelling. This is commonly done by subset selection methods, which may be computationally intensive and even impractical when the true ARMA orders of the underlying model are high. On the other hand, automatic variable selection methods based on regularization do not directly apply to this problem because the i...

Given observations on a stationary economie vector time series process we show that the best % periods ahead forecast (best in the sense of having minimal forecast error variance) of one of the variables can be consistently estimated by nonparametric regression on an ARMA memory index. Our approach is based on a combination of the ARMA memory index modeling approach of Bierens (1986a) with a mo...

The proposed ARCH and its extension model have brought a powerful tool for the study of stock market volatility as well as verify that a “high risk brings high-yield” and the “leverage effect” of stock market. This paper gives modeling analysis by using the ARCH group models; in the last ten years Shanghai’s index returns, concluded that there are significant “high-yield associated with high-ri...

Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...

A dynamic model for limited dependent variables is proposed, which estimation does not rely on simulation methods. A latent conditional mean function which is measurable with respect to past and observable information circumvents the solution of a T -dimensional integral and yields a simple and computationally parsimonious maximum likelihood estimation. It can be shown that the latent process i...