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Use Machine Learning to Predict Future Market Prices
Klhůfek, Michal ; Trchalík, Roman (referee) ; Holkovič, Martin (advisor)
This thesis discusses a market prediction system based on the data obtained from the historic tranzaction. The main goal was to use the techniques of technical analysis to create a more accurate estimation of market behavior in the future. The data obtained from the current state of the market are compared with the historical market values using the algorithms for the classification of data from the field of learning. Based on individual algorithms, the software was designed to try to match the two sets of data as closely as possible. Testing took place on a dataset that represented the past market enthusiasm, and how much the overall system is performing.
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Information Retrieval in Text Data
Tkadlčík, Luboš ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis researches the issue of text data mining and information retrieval. It describes the most common representations of text documents and retrieval strategies. The aim of this thesis is design and implementation of application, which realises information retrieval via vector space model. The application implements three different ways of similarity calculation: cosine measure, the Jaccard coefficient and the Dice coefficient. Achieved results are assessed. Possible continuance of the project is outlined.
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Use Machine Learning to Predict Future Market Prices
Klhůfek, Michal ; Trchalík, Roman (referee) ; Holkovič, Martin (advisor)
This thesis discusses a market prediction system based on the data obtained from the historic tranzaction. The main goal was to use the techniques of technical analysis to create a more accurate estimation of market behavior in the future. The data obtained from the current state of the market are compared with the historical market values using the algorithms for the classification of data from the field of learning. Based on individual algorithms, the software was designed to try to match the two sets of data as closely as possible. Testing took place on a dataset that represented the past market enthusiasm, and how much the overall system is performing.
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|
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Information Retrieval in Text Data
Tkadlčík, Luboš ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis researches the issue of text data mining and information retrieval. It describes the most common representations of text documents and retrieval strategies. The aim of this thesis is design and implementation of application, which realises information retrieval via vector space model. The application implements three different ways of similarity calculation: cosine measure, the Jaccard coefficient and the Dice coefficient. Achieved results are assessed. Possible continuance of the project is outlined.
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