Development of a Modelling Script of Time Series Suitable for Data Mining

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.

Improving the interpretability of the effects of environmental factors on abundance of fish stocks

“…SFish optimally adjusts auto-regressive models connecting the main interface to a script encoded in R (ARIMAScript.r). This script is a modification of the script described by Sanz-Fernández et al (2016) which uses the auto.arima function proposed by Hyndman and Khandakar (2008). This function finds the best ARIMA model according to the uncorrected and corrected Akaike information criteria and the Bayesian information criterion.…”