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Parkinson's disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients' responses to drug therapies and lead to better-targeted...
In this paper, a recently proposed time series forecasting algorithm, Modified Pattern-based Sequence Forecasting (MPSF), is compared with three other algorithms. These algorithms have been applied to predict energy consumption at individual EV charging outlets using real world data from the UCLA campus. Two of these algorithms, namely MPSF and k-Nearest Neighbor (kNN), are relatively fast and structurally...
In this paper we propose a boosting regression model for time series using BP network and SVR as basic learning methods. We first make brief introduction on BP network and SVR, then give the specific boosting regression algorithm with theoretical analysis. In the experiment, we use a time series data of wind-speed from a coal mine as a training set to verify the efficiency of our proposed method....
Since it is difficult to establish precise physical model of complex systems, time series prediction is often used to predict their health trend and running state. Aiming at online prediction, we proposed a new scheme to fix the problems of time series online prediction, which is based on LS-SVR model and incremental learning algorithm. The scheme includes two aspects. Firstly, by replacing single...
This paper surveyed a novel demand forecasting method in logistics management based on Support vector machine. Firstly, a sliding time window is built and data in the sliding time window are employed to construct the model. Then we set up the demand forecasting model based on support vector regression. Results showed that this model proves to be effective and applicable for the demand forecasting...
A new methodology base on Least Squares Support Vector Machine (LS-SVM) for the electric power system monthly load forecasting is presented. The presented algorithm embodies the the structural risk minimization(SRM) principle is more generalized performance and accurate as compared to artificial neural network. In the time series the trend component and periodical component are considered to make...
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...
Air temperature is closely related to life and affects all aspects of life. Therefore, the forecast of the temperature is more far-reaching. In this paper, a new model based on EMD (Empirical Mode Decomposition) and LS-SVM (Least Squares Support Vector Machine) was proposed. At first, EMD was applied to adaptively decomposing the time series into a series of different scales of intrinsic mode function...
The technology of phase space construction and Support Vector Machines(SVM) is introduced firstly. Then a novel complex time series forecasting approach based on SVM is proposed. The complex time series is decomposed into long-term trend series and short-term fluctuation series. The SVM regressive forecasting model is constructed respectively. The proposed forecasting approach is applied to the Shanghai...
Fuzzy model based on support vector machine(SVM-based fuzzy model) was proposed in recent years. Although SVM has an excellent generalization performance, it is considered to have lower computation speed, and a large number of support vectors may be found, which leads to a complex fuzzy model with too many rules. To deal with the problem, the paper presents a new approach called base vector learning(BVL)...
Economic forecasting has become an important research topic in field of management science. Economic operation is a complex and changeable thing. There are many factors, which impact development of economy positively or negatively. This fact makes the economic system have dynamic, non-linear and uncertain characteristics. In this paper, a forecasting method is proposed for economic research, based...
The theories of phase space reconstruction and Support Vector Machines (SVM) are introduced firstly. A novel time series forecasting model based on wavelet and SVM is proposed. It first performances multi-scaled decomposition on complex time series using discrete wavelet transformation. Then the reconstructed approximate series and detail series are forecasted respectively using SVM. Finally, the...
Chaotic behaviour has been shown to exist in financial data. This paper advances the use of the sparse kernel machine model for the prediction of directional change for this class of dynamical systems. The notions of low entropy trajectory sets and low entropy trajectory balls in phase space are defined as the building patterns for the predictor. The statistical stability and robustness of the sparse...
Sparse support vector regression (SpSVR) method is proposed to improve the leaning speed without decreasing generalization performance. Firstly, the primal problem of support vector regression is directly optimized through Newton optimization method. Then, in order to realize the sparseness of SVR, Cholesky decomposition is used to update the Hessian matrix in SVR primal problem. Finally, such proposed...
Least Squares Support Vector Machine (LS-SVM) is a classic algorithm for regression estimation and classification. But unfortunately, for really large problems, LS-SVM can become highly memory and time consuming. In this paper, we present a simplified algorithm for LS-SVM, called ILS-SVM, which effectively reduces the algorithmic complexity. In order to improve the rate of convergence and overcome...
Motivated by the great success of dynamic time warping (DTW) in time series matching, Gaussian DTW kernel had been developed for support vector machine (SVM)-based time series classification. Counter-examples, however, had been subsequently reported that Gaussian DTW kernel usually cannot outperform Gaussian RBF kernel in the SVM framework. In this paper, by extending the Gaussian RBF kernel, we propose...
In recent years, Support Vector Regression (SVR) is used widely in predication field, with the advantages of structural risk minimization and strong generalization ability, which acquires good effects. The training characters of SVR model is the essential problem of affecting model accuracy. To solve the problem, this paper puts forward SVR model training method based on wavelet multi-resolution analysis,...
A clustering based composite kernels support vector machine ensemble forecasting model is proposed for the chaotic time series prediction. First, fuzzy possibility c-mean clustering algorithm (FPCM) is adopted to partition the input dataset into several subsets, which can overcome the drawback caused by outlier and noise in conventional fuzzy c-mean method. Then, SVMs with composite kernels that best...
Support vector machines, which are based on statistical learning theory and structural risk minimization principle, in theory, ensure the maximum generalization ability of the model. So compared with the neural network model established on the Empirical Risk Minimization principle, they are more comprehensive in theory. In this paper, it applies the support vector machine into building the time series...
Inspired by the so-called "divide-and-conquer" principle that is often used to attack a complex problem by dividing it into simpler problems, a three-stage SVM ensemble algorithm is proposed to improve its prediction accuracy and generalization performance for chaotic time series. In the first stage, Fuzzy C-means clustering algorithm is adopted to partition the input dataset into several...
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