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Ventura, S., Silva, M., Pérez-Bendito, D. and Hervas, C. (1995) Artificial Neural Networks for Estimation of Kinetic Analytical Parameters. Analytical Chemistry, 67, 1521-1525.
http://dx.doi.org/10.1021/ac00105a007

has been cited by the following article:

  • TITLE: Development of a Modelling Script of Time Series Suitable for Data Mining

    AUTHORS: Víctor Sanz-Fernández, Remedios Cabrera, Rubén Muñoz-Lechuga, Antonio Sánchez-Navas, Ivone A. Czerwinski

    KEYWORDS: Data Mining, ARIMA Models, Time Series, Script, R

    JOURNAL NAME: Open Journal of Statistics, Vol.6 No.4, July 22, 2016

    ABSTRACT: Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, economics, etc.). In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis (time-series), free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others. Therefore, ARIMA models appear as a Data Mining technique, offering reliable, robust and high-quality results, to help validate and sustain the research carried out.