2023
Moreno-Pino, Fernando; Olmos, Pablo M; Artés-Rodríguez, Antonio
Deep Autoregressive Models with Spectral Attention Artículo de revista
En: Pattern Recognition, pp. 109014, 2023, ISSN: 0031-3203.
Resumen | Enlaces | BibTeX | Etiquetas: Attention models, Deep learning, Filtering, global-local contexts, Signal processing, spectral domain attention, time series forecasting
@article{MORENOPINO2022109014,
title = {Deep Autoregressive Models with Spectral Attention},
author = {Fernando Moreno-Pino and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S0031320322004940},
doi = {https://doi.org/10.1016/j.patcog.2022.109014},
issn = {0031-3203},
year = {2023},
date = {2023-01-01},
urldate = {2022-01-01},
journal = {Pattern Recognition},
pages = {109014},
abstract = {Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model’s embedded space. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series’s noise. The proposed architecture has a number of useful properties: it can be effectively incorporated into well-known forecast architectures, requiring a low number of parameters and producing explainable results that improve forecasting accuracy. We test the Spectral Attention Autoregressive Model (SAAM) on several well-known forecast datasets, consistently demonstrating that our model compares favorably to state-of-the-art approaches.},
keywords = {Attention models, Deep learning, Filtering, global-local contexts, Signal processing, spectral domain attention, time series forecasting},
pubstate = {published},
tppubtype = {article}
}
2013
Perez-Cruz, Fernando; Vaerenbergh, Steven Van; Murillo-Fuentes, Juan Jose; Lazaro-Gredilla, Miguel; Santamaria, Ignacio
Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances Artículo de revista
En: IEEE Signal Processing Magazine, vol. 30, no 4, pp. 40–50, 2013, ISSN: 1053-5888.
Resumen | Enlaces | BibTeX | Etiquetas: adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication
@article{Perez-Cruz2013,
title = {Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances},
author = {Fernando Perez-Cruz and Steven Van Vaerenbergh and Juan Jose Murillo-Fuentes and Miguel Lazaro-Gredilla and Ignacio Santamaria},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6530761},
issn = {1053-5888},
year = {2013},
date = {2013-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {30},
number = {4},
pages = {40--50},
abstract = {Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning but are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with nonstationarity, low-complexity solutions, non-Gaussian noise models, and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.},
keywords = {adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication},
pubstate = {published},
tppubtype = {article}
}