Dữ liệu biên mục

Contributions to Data Analytics Techniques with Applications in Forecasting, Visualization and Decision Support
Loại tài liệu: Book
Mã tài liệu: 79467
Mã ngôn ngữ: en
Thông tin xuất bản: Gottfried Wilhelm Leibniz Universität Hannover
Tình trạng vật lý: 96 p.
Từ khóa: Reinforcement Learning, Artificial Neural Networks, SentimentAnalysis, Leasing, Used Cars, Feature Engineering, Domain Knowledge, Visualization
Danh mục: Khoa học thư viện, thông tin, xuất bản, Khoa học xã hội và hành vi, Sách
Môn học: (07TDPTD) Ngành Phân tích dữ liệu kinh doanh, (INS3083) Phân tích và trực quan hóa dữ liệu, (08-TDHKHXHNV-TLTK) Tài liệu tham khảo
Năm xuất bản: 2018
Số sách còn lại: Không giới hạn
Thời gian mượn: Bạn chưa đăng nhập.
Tóm tắt theo nội dung: 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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