@article{JIMENEZ-NAVARRO23a,
title = {From Simple to Complex: A Sequential Method for Enhancing Time Series Forecasting with Deep Learning},
author = {M. J. Jiménez-Navarro and M. Martínez-Ballesteros and F. Mártinez-Álvarez and A. Troncoco and G. Asencio-Cortés},
year = {2023},
date = {2023-01-20},
journal = {Logic Journal of the IGPL},
volume = {in press},
abstract = {Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished.Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer (TFT), NBEATS and Echo State Network (ESN).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{MOLINA22,
title = {DIAFAN-TL: An instance weighting-based transfer learning algorithm with application to phenology forecasting},
author = {M. Á. Molina and M. J. Jiménez-Navarro and R. Arjona and F. Mártinez-Álvarez and G. Asencio-Cortés},
url = {https://www.sciencedirect.com/science/article/pii/S0950705122008322},
doi = {https://doi.org/10.1016/j.knosys.2022.109644},
year = {2022},
date = {2022-10-22},
journal = {Knowledge-Based Systems},
volume = {254},
pages = {109644},
abstract = {The agricultural sector has been, and still is, the most important economic sector in many countries. Due to advances in technology, the amount and variety of available data have been increasing over the years. However, compared to other economic sectors, there is not always enough quality data for one particular domain (crops, plantations, plots) to obtain acceptable forecasting results with machine learning algorithms. In this context, transfer learning can help extract knowledge from different but related domains with enough data to transfer it to a target domain with scarce data. This process can overcome forecasting accuracy compared to training models uniquely with data from the target domain. In this work, a novel instance weighting-based transfer learning algorithm is proposed and applied to the phenology forecasting problem. A new metric named DIAFAN is proposed to weight samples from different source domains according to their relationship with the target domain, promoting the diversity of the information and avoiding inconsistent samples. Additionally, a set of validation schemes is specifically designed to ensure fair comparisons in terms of data volume with other benchmark transfer learning algorithms. The proposed algorithm, DIAFAN-TL, is tested with a proposed dataset of 16 plots of olive groves from different places, including information fusion from satellite images, meteorological stations and human field sampling of crop phenology. DIAFAN-TL achieves a remarkable improvement with respect to 15 other well-known transfer learning algorithms and three nontransfer learning scenarios. Finally, several performance analyses according to the different phenological states, prediction horizons and source domains are also performed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{TORRES21b,
title = {Electricity consumption time series forecasting using Temporal Convolutional Networks},
author = {J. F. Torres and M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso},
year = {2021},
date = {2021-09-01},
booktitle = {Conference of the Spanish Association for Artificial Intelligence (CAEPIA'21)},
series = {Lecture Notes in Artificial Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{JIMENEZ-NAVARRO21,
title = {HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting},
author = {M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés},
doi = {https://doi.org/10.1007/978-3-030-87869-6_68},
year = {2021},
date = {2021-09-01},
booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications},
volume = {1401},
pages = {717-727},
series = {Advances in Intelligent Systems and Computing},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{MOLINA21,
title = {A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery},
author = {M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés},
url = {https://link.springer.com/chapter/10.1007/978-3-030-86271-8_43},
doi = {https://doi.org/10.1007/978-3-030-86271-8_43},
year = {2021},
date = {2021-09-01},
booktitle = {HAIS},
volume = {12886},
pages = {511-523},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}