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Mostrando ítems 1-10 de 12
Capítulo de Libro
Partitioning-Clustering Techniques Applied to the Electricity Price Time Series
(2007)
Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract ...
Artículo
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
(World Scientific, 2021)
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a ...
Capítulo de Libro
Quantitative Association Rules Applied to Climatological Time Series Forecasting
(2009)
This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine ...
Ponencia
Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation
(Springer, 2022)
Most of the current data sources generate large amounts of data over time. Renewable energy generation is one example of such data sources. Machine learning is often applied to forecast time series. Since data flows are ...
Capítulo de Libro
LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
(2008)
A new approach is presented in this work with the aim of predicting time series behaviors. A previous labeling of the samples is obtained utilizing clustering techniques and the forecasting is applied using the information ...
Capítulo de Libro
Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences
(2009)
This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes techniques as a complementary step to the model. When real-world time series are forecasted, there exist ...
Artículo
An evolutionary algorithm to discover quantitative association rules in multidimensional time series
(Springer, 2011)
An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative ...
Artículo
Recent Advances in Energy Time Series Forecasting
(MDPI, 2017)
This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 ...
Ponencia
On the performance of deep learning models for time series classification in streaming
(Springer, 2020)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...
Artículo
Data streams classification using deep learning under different speeds and drifts
(Oxford University Press, 2022)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...