Moving Average (MA) operators are used in Box-Jenkins’s ARIMA models in time series analysis (1). We can used MA operators of structural descriptors are useful to quantify multiple conditions or parameters in complex datasets in Omics, Medicinal Chemistry, Nanotechnology, etc. (2-7). Speck-Planche and Cordeiro have also used this kind of models in multiple problems (8-11). In this work, we develop a desktop application that allows applying mathematical and statistical calculations in batches, on input and output variables selected by the user. From the obtained result a percentage sample of data is taken with a random contrast on which Machine Learning algorithms are applied.
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FRAMA 1.0: Framework for Moving Average Operators Calculation in Data Analysis
Published: 28 November 2017 by MDPI in MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed. congress USEDAT-03: USA-EU Data Analysis Training Prog. Work., Cambridge, UK-Bilbao, Spain-Duluth, USA, 2017
Keywords: Biga Data; Data Fusion; Data Analysis; Moving Average; Machine Learning