FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205650800> ?p ?o ?g. }

• W4205650800 endingPage "80" @default.
• W4205650800 startingPage "70" @default.
• W4205650800 abstract "The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. It is uncovered that scholars and practitioners face some difficulties in modelling complex system such as the stock market because it is nonlinear, chaotic, multi- dimensional, and spatial in nature, making forecasting a complex process. Models estimating nonstationary financial time series may include noise and errors. The relationship between the input and output parameters of the models is essentially non-linear, where stock prices include higher-level variables, which complicates stock market modeling and forecasting. It is also revealed that financial time series are multidimensional and they are influenced by many factors, such as economics, politics, environment and so on. Analysis and evaluation of multi- dimensional systems and their forecasting should be carried out by machine learning models. The problem of forecasting the stock market and obtaining quality forecasts is an urgent task, and the methods and models of machine learning should be the main mathematical tools in solving the above problems. First, we proposed to use self-organizing map, which is used to visualize multidimensional data by configuring neurons to quantize or cluster the input space in the topological structure. These characteristics of this algorithm make it attractive in solving many problems, including clustering, especially for forecasting stock prices. In addition, the methods discussed, encourage us to apply this cluster approach to present a different data structure for forecasting. Thus, models of adaptive neuro-fuzzy inference system combine the characteristics of both neural networks and fuzzy logic. Given the fact that the rule of hybrid learning and the theory of logic is a clear advantage of adaptive neuro-fuzzy inference system, which has computational advantages over other methods of parameter identification, we propose a new hybrid algorithm for integrating self-organizing map with adaptive fuzzy inference system to forecast stock index prices. This algorithm is well suited for estimating the relationship between historical prices in stock markets. The proposed hybrid method demonstrated reduced errors and higher overall accuracy." @default.
• W4205650800 created "2022-01-25" @default.
• W4205650800 creator A5026214579 @default.
• W4205650800 creator A5026690687 @default.
• W4205650800 date "2021-01-01" @default.
• W4205650800 modified "2023-09-26" @default.
• W4205650800 title "HYBRID MODEL OF SELF-ORGANIZING MAP AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN STOCK INDEXES FORECASTING" @default.
• W4205650800 cites W1912463333 @default.
• W4205650800 cites W1980458013 @default.
• W4205650800 cites W2019207321 @default.
• W4205650800 cites W2161103910 @default.
• W4205650800 cites W2194689335 @default.
• W4205650800 cites W2767371034 @default.
• W4205650800 cites W4255427895 @default.
• W4205650800 cites W65738273 @default.
• W4205650800 doi "https://doi.org/10.31861/bmj2021.02.05" @default.
• W4205650800 hasPublicationYear "2021" @default.
• W4205650800 type Work @default.
• W4205650800 citedByCount "0" @default.
• W4205650800 crossrefType "journal-article" @default.
• W4205650800 hasAuthorship W4205650800A5026214579 @default.
• W4205650800 hasAuthorship W4205650800A5026690687 @default.
• W4205650800 hasBestOaLocation W42056508001 @default.
• W4205650800 hasConcept C111168008 @default.
• W4205650800 hasConcept C119857082 @default.
• W4205650800 hasConcept C122282355 @default.
• W4205650800 hasConcept C124101348 @default.
• W4205650800 hasConcept C149782125 @default.
• W4205650800 hasConcept C151406439 @default.
• W4205650800 hasConcept C151730666 @default.
• W4205650800 hasConcept C154945302 @default.
• W4205650800 hasConcept C186108316 @default.
• W4205650800 hasConcept C195975749 @default.
• W4205650800 hasConcept C2776214188 @default.
• W4205650800 hasConcept C2777052490 @default.
• W4205650800 hasConcept C2780299701 @default.
• W4205650800 hasConcept C2780762169 @default.
• W4205650800 hasConcept C33923547 @default.
• W4205650800 hasConcept C41008148 @default.
• W4205650800 hasConcept C49937458 @default.
• W4205650800 hasConcept C50644808 @default.
• W4205650800 hasConcept C58166 @default.
• W4205650800 hasConcept C73555534 @default.
• W4205650800 hasConcept C86803240 @default.
• W4205650800 hasConcept C88389905 @default.
• W4205650800 hasConceptScore W4205650800C111168008 @default.
• W4205650800 hasConceptScore W4205650800C119857082 @default.
• W4205650800 hasConceptScore W4205650800C122282355 @default.
• W4205650800 hasConceptScore W4205650800C124101348 @default.
• W4205650800 hasConceptScore W4205650800C149782125 @default.
• W4205650800 hasConceptScore W4205650800C151406439 @default.
• W4205650800 hasConceptScore W4205650800C151730666 @default.
• W4205650800 hasConceptScore W4205650800C154945302 @default.
• W4205650800 hasConceptScore W4205650800C186108316 @default.
• W4205650800 hasConceptScore W4205650800C195975749 @default.
• W4205650800 hasConceptScore W4205650800C2776214188 @default.
• W4205650800 hasConceptScore W4205650800C2777052490 @default.
• W4205650800 hasConceptScore W4205650800C2780299701 @default.
• W4205650800 hasConceptScore W4205650800C2780762169 @default.
• W4205650800 hasConceptScore W4205650800C33923547 @default.
• W4205650800 hasConceptScore W4205650800C41008148 @default.
• W4205650800 hasConceptScore W4205650800C49937458 @default.
• W4205650800 hasConceptScore W4205650800C50644808 @default.
• W4205650800 hasConceptScore W4205650800C58166 @default.
• W4205650800 hasConceptScore W4205650800C73555534 @default.
• W4205650800 hasConceptScore W4205650800C86803240 @default.
• W4205650800 hasConceptScore W4205650800C88389905 @default.
• W4205650800 hasIssue "2" @default.
• W4205650800 hasLocation W42056508001 @default.
• W4205650800 hasOpenAccess W4205650800 @default.
• W4205650800 hasPrimaryLocation W42056508001 @default.
• W4205650800 hasRelatedWork W1974708359 @default.
• W4205650800 hasRelatedWork W2137873534 @default.
• W4205650800 hasRelatedWork W2170083822 @default.
• W4205650800 hasRelatedWork W2289642014 @default.
• W4205650800 hasRelatedWork W2370239109 @default.
• W4205650800 hasRelatedWork W2541528416 @default.
• W4205650800 hasRelatedWork W2561237030 @default.
• W4205650800 hasRelatedWork W609444952 @default.
• W4205650800 hasRelatedWork W1629725936 @default.
• W4205650800 hasRelatedWork W1905011107 @default.
• W4205650800 hasVolume "9" @default.
• W4205650800 isParatext "false" @default.
• W4205650800 isRetracted "false" @default.
• W4205650800 workType "article" @default.