Browsing by Subject "Multivariate time series"

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    Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction
    (Elsevier, 2020-04) Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Sánchez, Gracia; Marín, David; Palacios, Francisco; López, Lucia; Ingeniería de la Información y las Comunicaciones
    AAntimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-ofthe-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision- making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making process provides a RMSE=(0.1349, 0.1304, 0.1325) and a MAE=(0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions.
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    MultiBEATS: blocks of eigenvalues algorithm for multivariate time series dimensionality reduction
    (Elsevier, 2024-04) González Vidal, Aurora; Martínez Ibarra, Antonio; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Multivariate Time Series are sequences of observations taken from multiple sources. The proliferation of environments in which data is collected by means of sensors and the adoption of data-based services reveals the importance of their efficient analysis. In smart environments, the analysis of the raw Multivariate Time Series is cumbersome. The algorithms are heavy, slow, and fail to extract all the knowledge available. In that sense, representation techniques reduce the data points but maintain the inner patterns so that the information present in data persists. There exist several approaches to represent Univariate Time Series, but for the multivariate case they are scarce. We have adapted the existent univariate representation algorithm BEATS, which is based on Discrete Cosine transformation and the extraction of eigenvalues to multivariate scenarios and we have called it MultiBEATS. Our method allows flexibility with regard to data reduction and does not require labelled data. To investigate the efficacy of MultiBEATS, we selected 8 open multivariate time series datasets and compared the running time and accuracy of their classification using raw data and MultiBEATS transformed data. We have also compared our results with the representation algorithm Seq2VAR, based on autoencoders, for having a baseline. The MultiBEATS transformation reduces the execution time of classification algorithms, has improved the results in accuracy in 2 of the datasets and matches a third one. With regards to the baseline, MultiBEATS is faster than Seq2VAR and more accurate in 7 out of the 8 datasets. In conclusion, MultiBEATS is a promising approach for efficiently reducing multivariate time series data, therefore helping decision-making in dynamic smart environments.