Report NEP-BIG-2021-04-12

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report:

• Shenhao Wang & Baichuan Mo & Yunhan Zheng & Stephane Hess & Jinhua Zhao, 2021, "Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark," Papers, arXiv.org, number 2102.01130, Feb, revised Mar 2025.
• Pratyush Muthukumar & Jie Zhong, 2021, "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers, arXiv.org, number 2102.01290, Feb.
• Dawid Siwicki, 2021, "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-05.
• Christian M. Dahl & Torben S. D. Johansen & Emil N. S{o}rensen & Christian E. Westermann & Simon F. Wittrock, 2021, "Applications of Machine Learning in Document Digitisation," Papers, arXiv.org, number 2102.03239, Feb.
• Igor Halperin, 2021, "Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)," Papers, arXiv.org, number 2104.01040, Apr.
• Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021, "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science, number 2104, Apr.
• David Pastor-Escuredo, 2021, "Future of work: ethics," Papers, arXiv.org, number 2104.02580, Apr.
• Aithal, Sreeramana & L. M., Madhushree, 2019, "Information Communication & Computation Technology (ICCT) as a Strategic Tool for Industry Sectors," MPRA Paper, University Library of Munich, Germany, number 105619, Nov.
• Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021, "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers, arXiv.org, number 2102.04382, Feb.
• Holger Breinlich & Valentina Corradi & Nadia Rocha & Michele Ruta & Joao M.C. Santos Silva & Tom Zylkin, 2021, "Machine Learning in International Trade Research ?- Evaluating the Impact of Trade Agreements," School of Economics Discussion Papers, School of Economics, University of Surrey, number 0521, Mar.
• Marzagão, Thiago & Ferreira, Rodrigo & Sales, Leonardo, 2021, "Real Estate Appraisal in Brazil," OSF Preprints, Center for Open Science, number zrgv6, Apr, DOI: 10.31219/osf.io/zrgv6.
• Ali Hirsa & Joerg Osterrieder & Branka Hadji Misheva & Wenxin Cao & Yiwen Fu & Hanze Sun & Kin Wai Wong, 2021, "The VIX index under scrutiny of machine learning techniques and neural networks," Papers, arXiv.org, number 2102.02119, Feb.
• Kevin Kuo & Ronald Richman, 2021, "Embeddings and Attention in Predictive Modeling," Papers, arXiv.org, number 2104.03545, Apr.
• Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021, "Forecasting with Deep Learning: S&P 500 index," Papers, arXiv.org, number 2103.14080, Mar.
• Mochammad Ridwan Ristyawan, 2021, "Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank," GATR Journals, Global Academy of Training and Research (GATR) Enterprise, number jfbr179, Mar, DOI: https://doi.org/10.35609/jfbr.2021..
• Dorsa Mohammadi Arezooji, 2021, "A Big Data Analysis of the Ethereum Network: from Blockchain to Google Trends," Papers, arXiv.org, number 2104.01764, Apr.
• Jaydip Sen & Sidra Mehtab, 2021, "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers, arXiv.org, number 2103.15096, Mar.
• Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021, "Stock price forecast with deep learning," Papers, arXiv.org, number 2103.14081, Mar.
• Dacre, Nicholas & Kockum, Fredrik & Senyo, PK, 2020, "Transient Information Adaptation of Artificial Intelligence: Towards Sustainable Data Processes in Complex Projects," SocArXiv, Center for Open Science, number pagbm, Sep, DOI: 10.31219/osf.io/pagbm.
• Zhong, Weifeng & Chan, Julian, 2020, "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers, George Mason University, Mercatus Center, number 10464, Feb.
• Livia Paranhos, 2021, "Predicting Inflation with Recurrent Neural Networks," Papers, arXiv.org, number 2104.03757, Apr, revised Oct 2023.
• Maksim Malyy & Zeljko Tekic & Tatiana Podladchikova, 2021, "The value of big data for analyzing growth dynamics of technology based new ventures," Papers, arXiv.org, number 2104.03053, Apr.
• Itay Goldstein & Chester S. Spatt & Mao Ye, 2021, "Big Data in Finance," NBER Working Papers, National Bureau of Economic Research, Inc, number 28615, Mar.
• Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler, 2021, "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python," Papers, arXiv.org, number 2104.03220, Apr, revised Dec 2021.
• Kunal Pattanayak & Vikram Krishnamurthy, 2021, "Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification," Papers, arXiv.org, number 2102.04594, Feb, revised Jul 2021.

Report NEP-BIG-2021-04-12

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: