Analysis of Forecasting Methods as a Tool for Information Structuring in Science Research
Olga Popova *
Kuban State Technological University, Krasnodar, Russian Federation
Boris Popov
Kuban State Technological University, Krasnodar, Russian Federation.
Vladimir Karandey
Kuban State Technological University, Krasnodar, Russian Federation
Marina Evseeva
Kuban State University, Krasnodar, Russian Federation
*Author to whom correspondence should be addressed.
Abstract
Aims: To study forecasting methods so as to identify a general approach to information structuring and to choose the most efficient and approximated to natural intelligence method.
Study Design: logical experiment that reveals the advantages and disadvantages of the forecasting methods.
Place and Duration of Study: Department of Information Systems and programming of the Kuban State Technological University.
Methodology: Logical experiment: mental simulation of the forecasting methods, of information structuring, knowledge and initial data representation, and the choice process in the studied methods followed by a written statement; inductive and deductive methods: comparing individual intuition methods and collective expert estimations to natural intelligence (NI), presenting the findings in Tables.
Results: The data collected as a result of logical experiment:
1. The more formalized a method is, the farther it is from NI and the readier for implementation by modern methods and means of computing equipment.
2. Eliminating any shortcomings of a forecasting method requires the approximation of its information structuring and choice making to those of NI.
3. Developing efficient DSSs for method searching requires the knowledge structuring similar to that of NI.
Conclusion: The existing forecasting methods are not suitable for intellectual DSSs. This paper suggests that science research should use new intelligence enhancement methods (IEM) when structuring information and formulating problems to be solved.
Keywords: Forecasting methods analysis, intelligence enhancement, information structuring, natural intelligence, science research