Stock returns are continuously generated by different data sources and depend on various factors such as financial policies and national economic growths.Stock returns prediction, unlike traditional regression, requires consideration of both the sequential and interdependent nature of financial time-series. This work uses a two-stage approach, using kernel adaptive filtering (KAF) within a stock market interdependence approach to sequentially predict stock returns.Thus, unlike traditional KAF formulations, prediction uses not only their local models but also the individual local models learned from other stocks, enhancing prediction accuracy. The enhanced KAF plus market interdependence framework has been tested on 24 different stocks from major economies. The enhanced approach obtains higher sharpe ratio when compared with KAF-based methods, long short-term memory, and autoregressive-based models.

A learning task is sequential if its data samples become available over time; kernel adaptive filters (KAFs) are sequential learning algorithms. There are three main challenges in KAFs: (1) selection of an appropriate Mercer kernel; (2) the lack of an effective method to determine kernel-sizes in an online learning context; (3) how to tune the step-size parameter. This work introduces a framework for online prediction that addresses the latter two of these open challenges. The kernel-sizes, unlike traditional KAF formulations, are both created and updated in an online sequential way. Further, to improve convergence time, we propose an adaptive step-size strategy that minimizes the mean-square-error (MSE) using a stochastic gradient algorithm. The proposed framework has been tested on three real-world data sets; results show both faster convergence to relatively low values of MSE and better accuracy when compared with KAF-based methods, long short-term memory, and recurrent neural networks.

Abstract.A methodology for automatic segmentation and classification of multi-channel data related to motion capture (MoCap) videos of cyclic activities are presented. Regarding this, a kernel approach is employed to obtain a time representation, which captures the cyclic behavior of a given multi-channel data. Moreover, we calculate a mapping based on kernel principal component analysis, in order to obtain a lowdimensional space that encodes the main cyclic behaviors. From such, low-dimensional space the main segments of the studied activity are inferred. Then, a distance based classifier is used to classified each MoCap video segment. A well-known MoCap database is tested which contains different activities performed by humans. Attained results shows how our approach is a simple alternative to obtain a suitable classification performance in comparison to complex methods for MoCap analysis.

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This paper introduces a novel spectral clustering approach based on kernels to analyze time-varying data. Our approach is developed within a multiple kernel learning framework, which, in this case is assumed as a linear combination model. To perform such linear combination, weighting factors are estimated by a ranking procedure yielding a vector calculated from the eigenvectors-derivedclustering-method. Particularly, the method named kernel spectral clustering is considered. Proposed method is compared to some conventional spectral clustering techniques, namely, kernel k-means and min-cuts. Standard k-means as well. The clustering performance is quantified by the normalized mutual information and Adjusted Rand Index measures. Experimental results prove that proposed approach is an useful tool for both tracking and clustering dynamic data, being able to manage applications for human motion analysis.