Title	ECML PKDD 2018 Workshops [electronic resource] : MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018, Proceedings / edited by Carlos Alzate, Anna Monreale, Livio Bioglio, Valerio Bitetta, Ilaria Bordino, Guido Caldarelli, Andrea Ferretti, Riccardo Guidotti, Francesco Gullo, Stefano Pascolutti, Ruggero G. Pensa, Celine Robardet, Tiziano Squartini
Imprint	Cham : Springer International Publishing : Imprint: Springer, 2019
Edition	1st ed. 2019
Connect to	https://doi.org/10.1007/978-3-030-13463-1
Descript	X, 173 p. 70 illus., 50 illus. in color. online resource

This book constitutes revised selected papers from two workshops held at the 18th 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.

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