@article{Melgar2023b,

title = {Identifying novelties and anomalies for incremental learning in streaming time series forecasting},

author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A.Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0952197623005109},

doi = {https://doi.org/10.1016/j.engappai.2023.106326},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {123},

pages = {106326},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Melgar2023a,

title = {A novel distributed forecasting method based on information fusion and incremental learning for streaming time series},

author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S1566253523000635},

doi = {https://doi.org/10.1016/j.inffus.2023.02.023},

year  = {2023},

date = {2023-01-01},

journal = {Information Fusion},

volume = {95},

pages = {163-173},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jimenez2023,

title = {Streaming big time series forecasting based on nearest similar patterns with application to energy consumption},

author = {P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso},

url = {https://academic.oup.com/jigpal/advance-article-abstract/doi/10.1093/jigpal/jzac017/6534493?redirectedFrom=fulltext},

doi = {https://doi.org/10.1093/jigpal/jzac017},

year  = {2023},

date = {2023-01-01},

journal = {Logic Journal of the IGPL},

volume = {31},

number = {2},

pages = {255-270},

abstract = {This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbors algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbors algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and a forecasting model based on the nearest neighbors is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbors model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of 10 minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Melgar2023c,

title = {A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area},

author = {L. Melgar-García and F. Martínez-Álvarez and D. T. Bui and A. Troncoso},

url = {https://www.tandfonline.com/doi/full/10.1080/17538947.2023.2252401},

doi = {https://doi.org/10.1080/17538947.2023.2252401},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Digital Earth},

volume = {16},

number = {1},

pages = {3661-3679},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MELGAR21_NEUCOMb,

title = {A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture},

author = {L. Melgar-García and D. Gutiérrez-Avilés and M. T. Godinho and R. Espada and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0925231222006415},

doi = {https://doi.org/10.1016/j.neucom.2021.06.101},

year  = {2022},

date = {2022-01-01},

journal = {Neurocomputing},

volume = {500},

pages = {268-278},

abstract = {Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Melgar21_IS,

title = {Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach},

author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025521000220},

doi = {10.1016/j.ins.2020.12.089},

year  = {2021},

date = {2021-01-01},

journal = {Information Sciences},

volume = {558},

pages = {174-193},

abstract = {Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a new triclustering approach for data streams is introduced. It follows a streaming scheme of learning in two steps: offline and online phases. First, the offline phase provides a summary model with the components of the triclusters. Then, the second stage is the online phase to deal with data in streaming. This online phase consists in using the summary model obtained in the offline stage to update the triclusters as fast as possible with genetic operators. Results using three types of synthetic datasets and a real-world environmental sensor dataset are reported. The performance of the proposed triclustering streaming algorithm is compared to a batch triclustering algorithm, showing an accurate performance both in terms of quality and running times. 

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ-ALVAREZ20,

title = {Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model},

author = {F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme},

url = {https://www.liebertpub.com/doi/full/10.1089/big.2020.0051},

doi = {10.1089/big.2020.0051},

year  = {2020},

date = {2020-07-22},

journal = {Big Data},

volume = {8},

number = {4},

pages = {308-322},

abstract = {This work proposes a novel bioinspired metaheuristic, simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures or traveling rate are introduced into the model in order to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate and number of recoveries, the infected population gradually decreases. The Coronavirus Optimization Algorithm has two major advantages when compared to other similar strategies. Firstly, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Secondly, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multi-virus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}