Non-linear System Identification with Composite Relevance Vector Machines

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selectionPublicad

Support Vector Regression for Non-Stationary Time Series

The difficulty associated with building forecasting models for non-stationary and volatile data has necessitated the development and application of new sophisticated techniques that can handle such data. Interestingly, there are a lot of real-world phenomena where data that are “difficult to analyze” are generated. One of these is the stock market where data series generated are often hard to forecast because of their peculiar characteristics. In particular, the stock market has been referred to as a complex environment and financial time series forecasting is often tagged as the most challenging application of time series forecasting. In this study, a novel approach known as Support Vector Regression (SVR) for forecasting non-stationary time series was adopted and the feasibility of applying this method to five financial time series was examined. Prior to implementing the SVR algorithm, three different methods of transformation namely Relative Difference in Percentages (RDP), Z-score and Natural Logarithm transformations were applied to the data series and the best prediction results obtained along with the associated transformation technique was presented. Our study indicated that the Z-score transformation is the best scaling method for financial time series, exhibiting superior performance than the other two transformations on the basis of five different performance measures. To determine the optimum values of the SVR parameters, a cross-validation method was implemented. For this purpose, the value of C and ε was varied from 5 to 100, and 0.001 and 0.1 respectively. The cross-validation method, though computationally expensive, is better than other proposed techniques for determining the values of these parameters. Another highlight of this study is the comparison of the SVR results to that obtained using 5-day Simple Moving Averages (SMA). The SMA was selected as a comparative method because it has been identified as the most popular quantitative forecasting method used by US corporations. Discussions with financial analysts also suggest that the SMA is one of the widely used in the financial industry. The popularity of the SMA can be explained by the fact that it is easy and cheap to use and it produces forecasts that can be easily interpreted by econometricians and other interested practitioners

Support vector method for robust ARMA system identification

This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals andSVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.Publicad

Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures

In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.Comment: ICPR 2014, International Conference on Pattern Recognition, Stockholm : Sweden (2014

Learning non-linear time scales with Kernel γ-Filters

A family of kernel methods, based on the γ-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) γ-filter, but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel γ-filters. The improved performance in several application examples suggests that a more appropriate representation of signal states is achieved.Publicad

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Author name used in this publication: Chun-Tian Cheng2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe