dc.contributor.author | Maknickienė, Nijolė | |
dc.contributor.author | Vaškevičiūtė, Agnetė | |
dc.date.accessioned | 2023-09-18T16:52:55Z | |
dc.date.available | 2023-09-18T16:52:55Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 2029-1035 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/117602 | |
dc.description.abstract | Exponentially increasing amount of information, variety of data forms, growing a number of big data analysis ant prediction tools gives the new opportunities for business, but create needs for right decisions of selection. This study aims to make three-dimensional analysis of data extraction methods, forecasting methods and sentiment indexes. Historical numerical data and textual data from forex news expressed throw the two sentiment indexes are forecasting by econometric methods, Python text analysis and unique computational intelligence tool. Prediction of ensemble of Evolino recurrent neural networks (EERNN) is a distribution of expected values reflecting the probabilities of different states of market sentiments. The results are intended to individual investors needs and gives them opportunity of choice, which depends on what data is available, the accuracy of the prediction, how much time can be taken to make the prediction and that the forecaster has enough skills to use the appropriate IT tools. | eng |
dc.format.extent | p. 14-20 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.source.uri | http://journal.kolegija.lt/iitsbe/journal/IITSBE-2017-1(22).pdf | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:25282370/datastreams/COVER/content | |
dc.subject | VE01 - Aukštos pridėtinės vertės ekonomika / High value-added economy | |
dc.title | Comparison of sentiments data extraction and prediction | |
dc.type | Straipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed source | |
dcterms.references | 38 | |
dc.type.pubtype | S4 - Straipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed publication | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Verslo vadybos fakultetas / Faculty of Business Management | |
dc.subject.researchfield | S 004 - Ekonomika / Economics | |
dc.subject.researchfield | S 003 - Vadyba / Management | |
dc.subject.ltspecializations | L103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society | |
dc.subject.en | sentiment | |
dc.subject.en | evolino | |
dc.subject.en | forecasting | |
dc.subject.en | text classification | |
dc.subject.en | artificial intelligence | |
dcterms.sourcetitle | Innovative infotechnologies for science, business and education | |
dc.description.volume | vol. 1 (22) | |
dc.publisher.name | Vilnius Business College | |
dc.publisher.city | Vilnius | |
dc.identifier.elaba | 25282370 | |