Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyper-parameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

Antibiotic-resistant bacteria pose a global health threat. Traditional antibiotics can lose their effectiveness, and the development of novel effective antimicrobials has become a priority in recent years. In this area, plants represent an invaluable source of antimicrobial compounds with vast therapeutic potential. To review the full possible spectrum of plant antimicrobial agents (plant compounds, extracts and essential oils) discovered from 2016 to 2021 and their potential to decrease bacterial resistance. Their activities against bacteria, with special emphasis on multidrug resistant bacteria, mechanisms of action, possible combinations with traditional antibiotics, roles in current medicine and future perspectives are discussed. Studies focusing on the antimicrobial activity of compounds of plant origin and their mechanism of action against bacteria were identified and summarized, including contributions from January 2016 until January 2021. Articles were extracted from the Medline database using PubMed search engine with relevant keywords and operators. The search yielded 11,689 articles from 149 countries, of which 101 articles were included in this review. Reports from 41 phytochemicals belonging to 20 families were included. Reports from plant extracts and essential oils from 39 plant species belonging to 17 families were also included. Polyphenols and terpenes were the most active phytochemicals studied, either alone or as a part of plant extracts or essential oils. Plasma membrane disruption was the most common mechanism of antimicrobial action. Number and position of phenolic hydroxyl groups, double bonds, delocalized electrons and conjugation with sugars in the case of flavonoids seemed to be crucial for antimicrobial capacity. Combinations of phytochemicals with beta-lactam antibiotics were the most studied, and the inhibition of efflux pumps was the most common synergistic mechanism. In recent years, terpenes, flavones, flavonols and some alkaloids and phenylpropanoids, either isolated or as a part of extracts, have shown promising antimicrobial activity, being membrane disruption their most common mechanism. However, their utilization as appropriate antimicrobials need to be boosted by means of new omics technologies and network pharmacology to find the most effective combinations among them or in combination with antibiotics.

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.