Martínez Ballesteros, María del Mar

Martínez Álvarez, Francisco

Reyes, Jorge

Riquelme Santos, José Cristóbal

Troncoso Lora, Alicia

English

idUS. Depósito de Investigación Universidad de Sevilla

EditorialApplications of Computational Intelligence in Time SeriesFrancisco Martínez-Álvarez,1 Alicia Troncoso,1 Jorge Reyes,2María Martínez-Ballesteros,3 and José C. Riquelme31Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain2NT2 Labs, Santiago, Chile3Department of Computer Science, University of Seville, Sevilla, SpainCorrespondence should be addressed to Francisco Mart́ınez-Álvarez; fmaralv@upo.esReceived 26 April 2017; Accepted 26 April 2017; Published 22 May 2017Copyright © 2017 Francisco Mart́ınez-Álvarez et al. is is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.e prediction of the future has fascinated the human beingsince its early existence. Actually, many of these eorts canbe noticed in everyday events such as energy management,telecommunications, pollution, bioinformatics, and seismol-ogy and, obviously, in neuroscience. Accurate predictionsare essential in economic activities as remarkable forecastingerrors in certain areas may involve large loss of money.In this context, the successful analysis of temporal datahas been a challenging task for many researchers duringthe last decades and, indeed, it is di	cult to gure out anyscientic branch with no time-dependent variables.Computational intelligence is known for including pow-erful techniques such as articial neural networks, fuzzysystems, evolutionary computation, learning theory, or prob-abilistic methods. us, this special issue has been focusedon the application of such techniques to time series. Inparticular, the goal was sharing recent advances in timeseries analysis and providing an interesting opportunity topresent and discuss the latest practical advances in real-worldapplications.Rigorous and extensive review processes have been car-ried out. Papers selected for this special issue present newndings and insights in the eld of time series forecasting.A broad range of topics has been discussed, especially in thefollowing areas: nances, tourism, feed grain demand, hazeepisodes, stock price, or data storage.Genetic algorithms have been developedwith binary cod-ing to analyze high-speed trading research using price data ofstocks on themicroscopic level.is problem is certainly newand unexplored from computational intelligence techniques.Reported results show that the system is able to improve theaccuracy for price movement forecasting, thus encouragingconducting research in this direction.Tourism demand forecasting has also been addressed bymeans of seasonal trend autoregressive integrated movingaverages with dendritic neural networks. Data from Japanare used to test the predictive performance of the proposedmodel. e model generates short-term predictions, aerapplying SARIMA models to exclude the long-term lineartrend. is study mixes linear and nonlinear models andsuggests that further analysis in the combination of suchtechniques is desirable.Likewise, a new irregular sampling estimation methodto extract the main trend of the time series has beenproposed. To achieve this, rst, the Kalman lter is used toremove dirty data. Second, the cubic spline interpolation andaverage method are used to reconstruct the main trend. eproposed approach has been applied to storage volume seriesof Internet data center. Results are quite promising.A novel hybrid articial intelligent system has beenproposed to forecast stock price index trend. In particular,articial neural networks combined with genetic algorithmshave been applied to data fromailand’s SET50 index trend,from years 2009 to 2014. Multiple features and dierent timespans have been considered. Comparisons to other methodsconrm the success of this novel methodology.e long-term prediction of feed grain demand issuehas been extensively discussed. A multivariate regressionHindawiComputational Intelligence and NeuroscienceVolume 2017, Article ID 9361749, 2 pageshttps://doi.org/10.1155/2017/93617492 Computational Intelligence and Neurosciencemodel along with a dynamic forecasting model has beenintroduced. Firstly, the correlation between the demand andits inuence factors are studied and, secondly, changes intrend in factors aecting the demand are forecasted. Reportedresults corroborate the eectiveness of the methodology.A novel long-term prediction model for Beijing hazeepisodes has been introduced. e authors have built adynamic structural measurement model of daily haze incre-ment and have reduced the model to a vector autoregressivemodel. Such model performs satisfactorily on next day’s airquality index forecasting, reaching in many cases an accuraterate close to 90%. erefore, sudden haze burst could bepredicted with this method.AcknowledgmentsFinally, we would like to thank all the authors for theirexcellent work and contributions to this special issue. Wewould also like to express our gratitude to all the reviewersfor their thorough revisions and patience in assisting us.Francisco Mart́ınez-ÁlvarezAlicia TroncosoJorge ReyesMaŕıa Mart́ınez-BallesterosJosé C. RiquelmeSubmit your manuscripts athttps://www.hindawi.comComputer Games  TechnologyInternational Journal ofHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Distributed  Sensor NetworksInternational Journal ofAdvances inFuzzySystemsHindawi Publishing Corporationhttp://www.hindawi.comVolume 2014International Journal ofReconfigurableComputingHindawi Publishing Corporation http://www.hindawi.com Volume 2014Hindawi Publishing Corporationhttp://www.hindawi.com Volume 201 Applied Computational Intelligence and Soft Computing Advances in Artificial IntelligenceHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Advances inSoftware EngineeringHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Electrical and Computer EngineeringJournal ofHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014 Advances in Multimedia International Journal of Biomedical ImagingHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Advances inHindawi Publishing Corporationhttp://www.hindawi.com Volume 201RoboticsJournal ofHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Computational Intelligence and NeuroscienceIndustrial EngineeringJournal ofHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Modelling & Simulation in EngineeringHindawi Publishing Corporation http://www.hindawi.com Volume 2014The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014Human-ComputerInteractionAdvances inComputer EngineeringAdvances inHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014

Applications of Computational Intelligence in Time Series

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Applications of Computational Intelligence in Time Series

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idUS. Depósito de Investigación Universidad de Sevilla