ECML PKDD 2018 Workshops (eBook, PDF)

MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018, Proceedings
Redaktion: Alzate, Carlos; Gullo, Francesco; Guidotti, Riccardo; Ferretti, Andrea; Caldarelli, Guido; Bordino, Ilaria; Bitetta, Valerio; Bioglio, Livio; Monreale, Anna; Squartini, Tiziano; Robardet, Celine; Pensa, Ruggero G.; Pascolutti, Stefano

• Format: PDF

Produktbeschreibung

This book constitutes revised selected papers from two workshops held at the 18^th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Ireland, in September 2018, namely:

MIDAS 2018 - Third Workshop on Mining Data for Financial Applications and PAP 2018 - Second International Workshop on Personal Analytics and Privacy.

The 12 papers presented in this volume were carefully reviewed and selected from a total of 17 submissions.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 173
• Erscheinungstermin: 6. Februar 2019
• Englisch
• ISBN-13: 9783030134631
• Artikelnr.: 58521760

Inhaltsangabe

A Multivariate and Multi-step ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting.- Calibrating the Mean-reversion Parameter in the Hull-White Model Using NeuralNetworks.- Deep Factor Model-Explaining Deep Learning Decisions for Forecasting Stock Returns with Layer-wise Relevance Propagation.- A Comparison of Neural Network Methods for Accurate Sentiment Analysis of Stock Market Tweets.- A Progressive Resampling Algorithm for Finding Very Sparse Investment Portfolios.- ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations.- Testing for Self-excitation in Financial Events: A Bayesian Approach.- A Web Crawling Environment to Support Financial Strategies and Trend Correlation.- A differential privacy workflow for inference of parameters in the Rasch model.- Privacy Preserving Client/Vertical-Servers Classification.- Privacy Risk for Individual Basket Patterns.- Exploring Students Eating Habits through Individual Profiling and Clustering Analysis.