## 5 Conclusions  
In this paper, we have made a comparison among different well-known models in architecture and their use in the prediction of electricity market prices. This comparison has been made by optimising the parameters for each of the algorithms and each of the datasets used. Given the temporal nature of the electricity price, we decided to study different time periods, specifically three, one of which served as a baseline (normal period), another period in which fraud was detected in the electricity market (fraud period) and finally the period that included the quarantine months in 2020, due to the pandemic (quarantine period). The results obtained have shown that the electricity market is clearly influenced by the social and economic circumstances that are occurring on a particular day, which is why it is important to take into account the ideosyncrasy of the data and use models in accordance with this fact. Of all the models used, three stand out: LSTM, CNN and Regression Trees. These three models are the ones that on average have given the best results for each of the validation metrics used, specifically, LSTM has obtained better results in the case of the quarantine period, CNN for the normal period and finally Trees for the fraud period. In terms of execution time, Deep Learning models have taken the longest, while Machine Learning models have taken the shortest. This is probably the biggest limitation in our study, the big difference between the time of the hyperparameter search and the training and validation time itself. However, once the hyperparameter search has been performed, the time taken to train the model and validate it is negligible. Given this limitation we also propose to look at this problem from the Data Streaming point of view, so that the model only has to be trained once. One of the disadvantages of neural networks is precisely that, the execution time they consume, however, they have proven to be a very good choice if a high percentage of prediction success is the objective of the task. In view of the results obtained, it can be said that for the specific case study we have dealt with, the Spanish electricity market, the results obtained with the different techniques are quite promising. As for which technique in particular is the best, we have to say that being such a complex problem there is no single technique that works well for all data sets, but what we can assure in view of the results is that Deep Learning techniques work well for this type of problem. This paper has focused on a time series study from a univariate point of view, i.e., the price is predicted taking into account only the past of this variable. As future work we propose the use of exogenous variables that have a direct correlation with price, such as demand [42], renewable energy capacity, balancing market price or trade value as used in [43], thus moving from a univariate to a multivariate problem. Testing with all possible variables is an almost impossible task, but the future objective we propose is to collect information on a wide range of variables and use only those that have a high degree of correlation with the variable to be predicted. Furthermore, given the large use of these models in the literature, we believe that it would be interesting to make use of ensemble technologies to make the final prediction, as this paradigm allows the advantages of individual models to be combined to make a final model. Conceptualization, B.V.-M. and A.A.-V.; methodology, B.V.-M.; software, B.V.-M.; validation, C.R.-E., I.A.N.-C. and A.A.-V.; formal analysis, B.V.-M.; investigation, B.V.-M.; resources, C.R.-E. and I.A.N.-C.; data curation, A.A.V. and B.V.-M.; writing--original draft preparation, B.V.-M.; writing--review and editing, C.R.-E., I.A.N.-C. and A.A.-V.; visualization, B.V.-M.; supervision, C.R.-E., I.A.N.-C. and A.A.-V.; project administration, C.R.-E. and I.A.N.-C.;funding acquisition, C.R.-E. and I.A.N.-C. All authors have read and agreed to the published version of the manuscript.