Browsing by Subject "Time series forecasting"

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    A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction
    (Elsevier, 2020-04-21) Jallal, Mohammed Ali; González Vidal, Aurora; Skarmeta Gómez, Antonio; Chabaa, Samira; Zeroual, Abdelouhab; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    The accuracy of the prediction of buildings’ energy consumption is being tackled using existing artificial intelligence techniques. However, there is a lack of effort on the development of new techniques for solving that problem and, therefore, achieving higher performance, which is important for the efficient management of energy in many levels. This study addresses this gap by proposing a new hybrid machine learning algorithm that incorporates the adaptive neuro-fuzzy inference system model with a new version of the firefly algorithm denominated as the gender-difference firefly algorithm. We expanded the search space diversification to increase the accuracy on the prediction and adopted the autoregressive process in order to approximate the chaotic behavior of the consumption time series. A new layer, denominated as non-working time adaptation was also integrated so as to decrease the fast variability of the predictions during non-working periods of time. We have applied our algorithm for the consumption prediction on 1 h, 2 h and 3 h ahead horizons. We have obtained improvements on the MAPE and R coefficient when compared with state-of-the-art publications in both a private dataset from the Faculty of Chemistry, located in the city of Murcia, Spain and a public dataset of the consumption of a Retail building located in California, United States. We also show our method’s performance in five more buildings. Our results demonstrate the robustness and the accuracy of our proposal when compared to the traditional adaptive neuro-fuzzy inference system models and also to the different predictive techniques implemented in several pieces of literature.
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    Multi-objective evolutionary feature selection for ensemble learning with random forests in time series forecasting
    (Elsevier, 2025-11-10) Espinosa, Raquel; Sánchez Carpena, Gracia; Palma Méndez, José Tomás; Jiménez Barrionuevo, Fernando; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Time series forecasting is fundamental in numerous domains, including finance, healthcare, energy, and environmental monitoring. However, the high dimensionality of feature spaces can lead to overfitting and reduced interpretability, making feature selection a crucial preprocessing step. This paper proposes a multiobjective evolutionary algorithm for feature selection in time series forecasting, designed to enhance predictive accuracy while improving generalization. The method partitions the dataset, associating each partition with an objective function in the optimization process. By independently selecting relevant feature subsets, it generates a Pareto front of Random Forest models, each trained on a distinct subset of features. These models are then aggregated into a stacking-based ensemble framework, effectively balancing feature relevance and diversity. Additionally, we introduce a feature importance measure based on selection frequency in the non-dominated solutions of the optimization process. To validate our approach, we conduct experiments on real-world forecasting tasks, including air quality prediction in southeastern Spain and Italy and oil temperature forecasting in industrial applications. We also evaluate performance on synthetic datasets of increasing complexity, systematically varying instances, features, seasonality, noise, and trends. The proposed method is compared against conventional Random Forest, a wrapper-based feature selection method with a multiobjective evolutionary search strategy, and several state-of-the-art embedded feature selection techniques for time series forecasting. The results demonstrate that our approach significantly improves forecasting accuracy while mitigating overfitting. By integrating multi-objetive evolutionary optimization, random forest, ensemble learning, and a novel feature importance measure, our method offers a robust, interpretable, and effective feature selection for time series forecasting applications.
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    Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data
    (Elsevier, 2022-12-10) Espinosa, Raquel; Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Feature selection wrapper methods are powerful mechanisms for reducing the complexity of prediction models while preserving and even improving their precision. Meta-heuristic methods, such as multi-objective evolutionary algorithms, are commonly used as search strategies in feature selection wrapper methods since they allow minimizing the cardinality of the attribute subset and simultaneously maximizing the predictive capacity of the model. However, in high-dimensional problems, multi-objective evolutionary algorithms for wrapper-type feature selection may require excessive computational time, sometimes impractical, especially when the learning algorithm has a high computational cost, such as deep learning. To address this drawback, in this paper we propose a multi-surrogate assisted multi-objective evolutionary algorithm for feature selection, specially designed to improve generalization error. The proposed method has been compared with conventional feature selection wrapper methods that use random forest, support vector machine and long short-term memory learning algorithms to evaluate subsets of attributes. The experiments have been carried out with regression and classification problems with time series data for air quality forecasting in the south-east of Spain and for indoor temperature forecasting in a domotic house. The results demonstrate the superiority of the proposed multi-surrogate assisted method over conventional wrapper methods using the same run times.
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    Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks
    (Elsevier, 2024-05-15) Espinosa Fernández, Raquel; Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Ingeniería de la Información y las Comunicaciones
    Surrogate-assisted multi-objective evolutionary algorithms are powerful techniques to solve computationally expensive multi-objective optimization problems. In this paper, we propose a direct fitness replacement method with generation-based fixed evolution control to implement a multi-objective evolutionary algorithm that uses a surrogate model for wrapper-type feature selection, where long short-term memory is established as the learning algorithm. The importance of the work and its benefits lie in the need to reduce the excessive computational time required by conventional wrapper-type feature selection methods based on multi-objective evolutionary algorithms and LSTM networks, maintaining or improving the predictive capacity of the models. We analyze the use of incremental learning to update the surrogate model, in comparison with the conventional non-incremental learning approach. We applied these methods in real-life time series forecasting of air quality, indoor temperature in a smart building and oil temperature in electricity transformers. Multi-step ahead predictions of the forecast models obtained with different meta-learners of the surrogate model were compared by using the Diebold–Mariano statistical test on a multi-criteria performance metric. The proposed method outperformed other approaches for feature selection including, among others, methods based on surrogate-assisted multi-objective evolutionary algorithms developed by the authors in previous research, other surrogate-assisted deterministic methods for feature selection and the conventional wrapper-type feature selection method based on LSTM, improving the prediction on test dataset by 23.98%, 34.61% and 13.77%, respectively.