dc.identifier.uri |
http://dx.doi.org/10.15488/4006 |
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dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/4040 |
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dc.contributor.author |
Eilers, Dennis
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dc.date.accessioned |
2018-11-21T13:49:39Z |
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dc.date.available |
2018-11-21T13:49:39Z |
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dc.date.issued |
2018 |
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dc.identifier.citation |
Eilers, Dennis: Contributions to data analytics techniques with applications in forecasting, visualization and decision support. Gottfried Wilhelm Leibniz Universität, Diss., 2018, XVIII, 96 S. DOI: https://doi.org/10.15488/4006 |
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dc.description.abstract |
This cumulative dissertation summarizes and critically discusses seven peer-reviewed publications where I was involved as a co-author. All publications contribute to data analytics techniques. The dissertation consists of four main sections. (1) Machine Leaning in Finance: In this section a Decision Support Algorithm based in Reinforcement Learning is introduced which filters rule-based trading decisions. We contribute to the literature by describing the implementation of the algorithm. We also provide empirical evidence of financial market anomalies. (2) Mining Customer Reviews: Opinions from customers about certain products are more and more expressed on social media platforms. Here we provide the first study which analyses YouTube comments as a data source for an aspect-based Sentiment Analysis. We also contribute to the literature by proposing a filtering method based on Google Trends which sorts product aspects according to their relevance for the customers. (3) Forecasting Resale Prices of Used Cars: In this section we show how to efficiently forecast resale prices of used cars with Artificial Neural Networks. We provide lessons learned about long-term forecasts. We also provide insights in the importance of certain independent factors which determine the resale price. (4) Visual Model Evaluation: The research in this section is mainly driven by the question of how to better incorporate human domain knowledge in data science. We develop a visualization technique based on heat maps which provides a more intuitive view on errors of a machine learning model. The visualization technique allows domain experts to discuss the results of machine learning models with data science experts on the same level of complexity. |
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dc.language.iso |
eng |
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dc.publisher |
Hannover : Institutionelles Repositorium der Leibniz Universität Hannover |
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dc.rights |
CC BY 3.0 DE |
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dc.rights.uri |
http://creativecommons.org/licenses/by/3.0/de/ |
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dc.subject |
Bestärkendes Lernen |
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dc.subject |
Künstliche Neuronale Netze |
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dc.subject |
Sentiment Analyse |
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dc.subject |
Leasing |
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dc.subject |
Gebrauchtfahrzeuge |
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dc.subject |
Feature Engineering |
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dc.subject |
Domain Knowledge |
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dc.subject |
Visualisierung |
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dc.subject.ddc |
330 | Wirtschaft
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dc.title |
Contributions to data analytics techniques with applications in forecasting, visualization and decision support |
eng |
dc.type |
DoctoralThesis |
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dc.type |
Text |
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dcterms.extent |
XVIII, 96 S. |
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dc.description.version |
publishedVersion |
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tib.accessRights |
frei zug�nglich |
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