The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical...
Data presented in form of time series as its analysis and applications recently have become increasingly important in different areas and domains. In this paper brief overview of some recently important standard problems, activities and models necessary for time series analysis and applications are presented. Paper also discusses some specific practical applications.
A new hybrid model that combines wavelet transform(WT) and least squares support vector machines(LSSVM) called the wavelet least squares support vector machines(WT-LSSVM) model is proposed and applied for runoff time series prediction. Time series of monthly runoff of Tangnaihai Station located in Yellow River upper stream were analyzed by the WT-LSSVM model. The observed time series are decomposed...
Wind power prediction has received much attention due to the development renewable energy sources using wind power. The paper presents a new approach which is a support vector regression (SVR) based local predictor (LP) with false neighbours filtered (FNF-SVRLP) to undertake short-term wind power perdition. The proposed predication method not only combines the powerful SVR with the reconstruction...
One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive...
Complex systems are complex, evolutionary, and dynamical system. One general method to predict such systems is use the previous and most recently behavior of a system to predict its future changes. The main advantage of this method is the ability to predict the behavior of systems without analytical prediction rules. In this situation, decision makers are often presented with several competing forecasts...
Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined...
Crustal deformation time series is a significant information source during the researches on continental deformation. In order to simulate the low frequency linear components which reflect dynamic trend as well as the high-frequency non-linear components which reflect disturbance, a prediction model based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented. With optimization...
Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resource prediction. In order to obtain better prediction performance, SVR's parameters must be selected carefully. Therefore, a particle swarm optimization-based SVR (PSO-SVR) model, in which PSO is used to determine free...
P300 is one of the most studied components of event related potentials which reflects the responses of brain to events in the external environment. In this paper, we present a new method that utilizes multiresolution autoregression of multichannel time series (MAMTS) for feature extraction of P300 wave. First, it adopts multiresolution autoregression on dyadic tree to depict the characteristic of...
Exchange rate time series is often characterized as chaotic in nature. The prediction using conventional statistical techniques and neural network with back propagation algorithm, which is most widely applied, do not give reliable prediction results. Exchange-rate time series is also a dynamic non-linear system, whose characteristics cannot be reflected by the static neutral network. The Nonlinear...
Since the implementation of the new mechanism of Renminbi exchange rate from 2005, the CNY/USD exchange rate fluctuation range has become more greater than before. Therefore, it is very important to control CNY/USD exchange rate risk via prediction. This paper is motivated by evidence that different prediction models can complement each other in approximating data sets, and presents a hybrid prediction...
This paper constructs an admissible manifold wavelet kernel (MWK) for support vector machine (SVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity (GARCH) model. The MWK is obtained by incorporating the wavelet technique and manifold theory into SVM. Unlike Gaussian kernel in SVM, the MWK can approximate arbitrary nonlinear functions...
Time series statistical models such as autoregressive moving average (ARMA) were considered useful in describing the texture and contextual information of an remote sensing image. To simplify the computation, we use a two-dimensional (2-D) autoregressive (AR) model instead. In our previous research, the 2-D univariate time series based imaging model was derived mathematically to extract the features...
We apply fuzzy techniques for system identification and supervised learning in order to develop fuzzy inference based autoregressors for time series prediction. An automatic methodology framework that combines fuzzy techniques and statistical techniques for nonparametric residual variance estimation is proposed. Identification is performed through the learn from examples method introduced by Wang...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.