Publications

@article{PEREZ24,

title = {Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption},

author = {R. Pérez-Chacón and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X23004752},

doi = {https://doi.org/10.1016/j.future.2023.12.021},

year  = {2024},

date = {2024-01-29},

journal = {Future Generation Computer Systems},

volume = {154},

pages = {397-412},

abstract = {Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.},

keywords = {big data, energy, forecasting, time series},

pubstate = {published},

tppubtype = {article}

}

@article{GARCIA-SOTO24,

title = {Water consumption time series forecasting in urban centers using deep neural networks},

author = {C. G. García-Soto and J. F. Torres and M. A. Zamora-Izquierdo and J. Palma and A. Troncoso},

url = {https://link.springer.com/article/10.1007/s13201-023-02072-4},

doi = {https://doi.org/10.1007/s13201-023-02072-4},

year  = {2024},

date = {2024-01-12},

journal = {Applied Water Science},

volume = {14},

pages = {1-14},

abstract = {The time series analysis and prediction techniques are highly valued in many application felds, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for diferent time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifcally designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.},

keywords = {deep learning, forecasting, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TRONCOSO-GARCIA23b,

title = {Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting},

author = {A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0168169923007755},

doi = {https://doi.org/10.1016/j.compag.2023.108387},

year  = {2023},

date = {2023-11-08},

journal = {Computers and Electronics in Agriculture},

volume = {215},

pages = {108387},

abstract = {Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time},

keywords = {deep learning, forecasting, precision agriculture, XAI},

pubstate = {published},

tppubtype = {article}

}