Fr. 76.00

Mining Data for Financial Applications - 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers

Inglese · Tascabile

Spedizione di solito entro 1 a 2 settimane (il titolo viene stampato sull'ordine)

Descrizione

Ulteriori informazioni

This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.*The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain.
*The workshop was held virtually due to the COVID-19 pandemic.
"Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets" and "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Sommario

Trade Selection with Supervised Learning and Optimal Coordinate Ascent (OCA).- How much does Stock Prediction improve with Sentiment Analysis?.- Applying Machine Learning to Predict Closing Prices in Stock Market: a case study.- Financial Fraud Detection with Improved Neural Arithmetic Logic Units.- Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets.- Multi-Objective Particle Swarm Optimization for Feature Selection in Credit Scoring.- A comparative analysis of Temporal Long Text Similarity: Application to Financial Documents.- Ranking Cryptocurrencies by Brand Importance: a Social Media Analysis in ENEAGRID.- Towards the Prediction of Electricity Prices at the Intraday Market Using Shallow and Deep-Learning Methods.- Neither in the Programs Nor in the Data: Mining the Hidden Financial Knowledge with Knowledge Graphs and Reasoning.- Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting.

Dettagli sul prodotto

Con la collaborazione di Valerio Bitetta (Editore), Ilari Bordino (Editore), Ilaria Bordino (Editore), Andrea Ferretti (Editore), Andrea Ferretti et al (Editore), Francesco Gullo (Editore), Giovanni Ponti (Editore), Lorenzo Severini (Editore)
Editore Springer, Berlin
 
Lingue Inglese
Formato Tascabile
Pubblicazione 25.03.2021
 
EAN 9783030669805
ISBN 978-3-0-3066980-5
Pagine 151
Dimensioni 155 mm x 9 mm x 235 mm
Illustrazioni X, 151 p. 64 illus., 50 illus. in color.
Serie Lecture Notes in Computer Science
Lecture Notes in Artificial Intelligence
Categoria Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica

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