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Modeliranje trendov kriptovalutnih trgov z uporabo tekstovnih podatkov
FELE, BENJAMIN (Author), Curk, Tomaž (Mentor) More about this mentor... This link opens in a new window

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Abstract
Cilj diplomskega dela je izgradnja modela, ki napoveduje trende vrednosti kriptovalut na podlagi podatkov spletne borze Poloniex. Problem je zanimiv zaradi potencialnih možnosti avtomatskega trgovanja s kriptovalutami, ki bi jih uspešen model omogočal. Za napovedovanje smo uporabili novice iz obravnavanega področja, ki so bile pridobljene iz spletne strani Reddit. Poleg tekstovnih podatkov so za napovedovanje uporabljeni tudi numerični podatki vrednosti kriptovalut pred objavo novice. Problem smo zastavili kot trirazredno klasifikacijo. Z uporabo metode TF-IDF, informacije o sentimentu, polariteti ter podatkih o člankih in stanju trga pred objavo besedila je bila dosežena 50,8% klasifikacijska točnost na validacijski množici ter 49,3% klasifikacijska točnost na testni množici. Za učenje in napovedovanje smo uporabili metodo podpornih vektorjev. Kljub relativno dobri klasifikacijski točnosti, model v praksi verjetno ne bi dajal dobičkonosnih napovedi.

Language:Slovenian
Keywords:tekstovno rudarjenje, podatkovno modeliranje, metoda podpornih vektorjev, kriptovalute, borza
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2017
Views:677
Downloads:278
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Secondary language

Language:English
Title:Modeling cryptocurrency market trends using textual data
Abstract:
The aim of this work is to build a model that predicts cryptocurrency trends based on data from the Poloniex online market. The problem is interesting because of the potential for automated cryptocurrency trading that a successful model would allow. For the forecast, we used the news from the subject area, which were obtained through the Reddit website. In addition to textual data, numerical market data before publishing the news is also used for forecasting. We approached the problem as a three-class classification problem. Using the TF-IDF method, sentiment information, polarity, and article and market information before the publication of the text, we achieved 50,8% classifying accuracy on the validation set and 49,3% classification accuracy on the test set. We used support vector machines for learning and prediction. We have found that in practice, despite the significant classification accuracy, the model is unlikely to yield profitable returns.

Keywords:text mining, data modeling, support vector machines, cryptocurrencies, stock market

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