Open AccessMethodological Approaches to Data Pre-Processing Formalization for Statistical Analysis
Author(s) -
D A Pisareva,
Е А Пахомова,
O.V. Rozhkova
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012022
Subject(s) - theme (computing) , computer science , data science , process (computing) , interpretation (philosophy) , adaptation (eye) , data processing , management science , data mining , operations research , mathematics , psychology , programming language , engineering , neuroscience , operating system
The topic of data processing is not accidental for us. Initially, we were engaged in the development of mathematical tools for assessing the impact of the University of Science city on the effectiveness of Regional development (USR). The theme turned out to be similar to the Triple Helix (TH) model of H. Etzkowitz. The main difference of the TH from the USR: the TH gives deep processes interpretations, the USR – the economic-mathematical modeling tools. In our opinion both models supplement each other: the USR interpretation results become brighter by means of the TH formulations. Then we began to develop a toolkit for the TH model with its adaptation to the conditions of Russia. Our articles are available on the Official Website of the Triple Helix Association Russian Chapter. It was important to adapt the apparatus of continuous functions to discrete functions, since official statistics are discrete in nature. Working with statistics revealed methodological problems. The paper offers methodological approaches to formalizing data pre-processing (the method of time series linking) in order to unify them and to further process them by modeling and forecasting. It seems that this formalization eliminates the appropriate methodological flaw in the literature.