MIDAS/PAP@PKDD/ECML

Keyphrases

Publications

volume 11054, 2019

ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018, Proceedings MIDAS/PAP@PKDD/ECML 11054 (2019)

2018

Ali Caner Türkmen, Ali Taylan Cemgil
Testing for Self-excitation in Financial Events: A Bayesian Approach. MIDAS/PAP@PKDD/ECML (2018)
Narges Tabari, Armin Seyeditabari, Tanya Peddi, Mirsad Hadzikadic, Wlodek Zadrozny
A Comparison of Neural Network Methods for Accurate Sentiment Analysis of Stock Market Tweets. MIDAS/PAP@PKDD/ECML (2018)
Derian Boer, Zahra Ahmadi, Stefan Kramer
Privacy Preserving Client/Vertical-Servers Classification. MIDAS/PAP@PKDD/ECML (2018)
Teresa Anna Steiner, David Enslev Nyrnberg, Lars Kai Hansen
A Differential Privacy Workflow for Inference of Parameters in the Rasch Model. MIDAS/PAP@PKDD/ECML (2018)
Marko Hassinen, Antti Ukkonen
A Progressive Resampling Algorithm for Finding Very Sparse Investment Portfolios. MIDAS/PAP@PKDD/ECML (2018)
Simon van der Zon, Wouter Duivesteijn, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy
ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations. MIDAS/PAP@PKDD/ECML (2018)
Giovanni Ponti, Giuseppe Santomauro, Fiorenzo Ambrosino, Giovanni Bracco, Antonio Colavincenzo, Matteo De Rosa, Agostino Funel, Dante Giammattei, Guido Guarnieri, Silvio Migliori
A Web Crawling Environment to Support Financial Strategies and Trend Correlation - - Extended Abstract -. MIDAS/PAP@PKDD/ECML (2018)
Michela Natilli, Anna Monreale, Riccardo Guidotti, Luca Pappalardo
Exploring Students Eating Habits Through Individual Profiling and Clustering Analysis. MIDAS/PAP@PKDD/ECML (2018)
Roberto Pellungrini, Anna Monreale, Riccardo Guidotti
Privacy Risk for Individual Basket Patterns. MIDAS/PAP@PKDD/ECML (2018)
Georgios Moysiadis, Ioannis Anagnostou, Drona Kandhai
Calibrating the Mean-Reversion Parameter in the Hull-White Model Using Neural Networks. MIDAS/PAP@PKDD/ECML (2018)
Jacopo De Stefani, Olivier Caelen, Dalila Hattab, Yann-Aël Le Borgne, Gianluca Bontempi
A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting. MIDAS/PAP@PKDD/ECML (2018)
Kei Nakagawa, Takumi Uchida, Tomohisa Aoshima
Deep Factor Model - Explaining Deep Learning Decisions for Forecasting Stock Returns with Layer-Wise Relevance Propagation. MIDAS/PAP@PKDD/ECML (2018)