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Forecasting financial time-series has long been among the most challenging problems in financial market analysis. In order to recognize the correct circumstances to enter or exit the markets investors usually employ statistical models (or even simple qualitative methods). However, the inherently noisy and stochastic nature of markets severely limits the forecasting accuracy of the used models. The...
The problem of approximate nearest neighbor (ANN) search in Big Data has been tackled with a variety of recent methods. Vector quantization based solutions have been maintaining the dominant position, as they operate in the original data space, better preserving inter-point distances. Additive quantization (AQ) in particular has pushed the state-of-the-art in search accuracy, but high computational...
In this paper, we formulate a variant of the Support Vector Machine classifier that exploits graph-based discrimination criteria within a multi-class optimization process. We employ two kNN graphs in order to describe intra-class and between-class data relationships. These graph structures are combined in order to form a regularizer which is used in order to regularize the multi-class SVM optimization...
A sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands using poles and coefficients of the RDSTFT spectra. Then, the sparsity is obtained by applying the Basis Pursuit (BP) algorithm on these frequency sub-bands. Finally, the total...
The standard median filter has only one tuning parameter — the width of the moving window on which it is based — and this has led to the development of a number of extremely useful extensons, including the recursive median filter, weighted median filters, and recursive weighted median filters. The Hampel filter is a member of the class of decision filters that, as we note here, may be viewed as another...
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