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Mining data for financial applicatio...
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MIDAS (Workshop) (2020 :)
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Mining data for financial applications = 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020 : revised selected papers /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Mining data for financial applications/ edited by Valerio Bitetta ... [et al.].
Reminder of title:
5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020 : revised selected papers /
remainder title:
MIDAS 2020
other author:
Bitetta, Valerio.
corporate name:
MIDAS (Workshop)
Published:
Cham :Springer International Publishing : : 2021.,
Description:
x, 151 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:
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.
Contained By:
Springer Nature eBook
Subject:
Data mining - Congresses. -
Online resource:
https://doi.org/10.1007/978-3-030-66981-2
ISBN:
9783030669812
Mining data for financial applications = 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020 : revised selected papers /
Mining data for financial applications
5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020 : revised selected papers /[electronic resource] :MIDAS 2020edited by Valerio Bitetta ... [et al.]. - Cham :Springer International Publishing :2021. - x, 151 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,125910302-9743 ;. - Lecture notes in computer science ;12591..
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.
"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.
ISBN: 9783030669812
Standard No.: 10.1007/978-3-030-66981-2doiSubjects--Topical Terms:
551626
Data mining
--Congresses.
LC Class. No.: QA76.9.D343 / M53 2020
Dewey Class. No.: 006.3
Mining data for financial applications = 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020 : revised selected papers /
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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.
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EB QA76.9.D343 M53 2020
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