Macroeconomic Forecasting in the Era of Big Data

Macroeconomic Forecasting in the Era of Big Data PDF Author: Peter Fuleky
Publisher: Springer Nature
ISBN: 3030311503
Category : Business & Economics
Languages : en
Pages : 719

View

Book Description
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1

Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1 PDF Author: Kohei Arai
Publisher: Springer Nature
ISBN: 3030899063
Category :
Languages : en
Pages :

View

Book Description


Machine Learning, Optimization, and Data Science

Machine Learning, Optimization, and Data Science PDF Author: Giuseppe Nicosia
Publisher: Springer Nature
ISBN: 3030954706
Category :
Languages : en
Pages :

View

Book Description


Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases PDF Author: Frank Hutter
Publisher: Springer Nature
ISBN: 3030676641
Category : Computers
Languages : en
Pages : 755

View

Book Description
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

An Alternative Proof of Minimum Trace Reconciliation

An Alternative Proof of Minimum Trace Reconciliation PDF Author: Mr. Sakai Ando
Publisher: International Monetary Fund
ISBN:
Category : Business & Economics
Languages : en
Pages : 12

View

Book Description
Minimum trace reconciliation, developed by Wickramasuriya et. al. (2019), is an innovation in the literature of forecast reconciliation. The proof, however, is indirect and not easy to extend to more general situations. This paper provides an alternative proof based on the first-order condition in the space of non-square matrix and argues that it is not only simpler but also can be extended to incorporate more general results on minimum weighted trace reconciliation in Panagiotelis et. al. (2021). Thus, our alternative proof not only has pedagogical value but also connects the results in the literature from a unified perspective.

Recent Applications of Financial Risk Modelling and Portfolio Management

Recent Applications of Financial Risk Modelling and Portfolio Management PDF Author: Škrinjari?, Tihana
Publisher: IGI Global
ISBN: 1799850846
Category : Business & Economics
Languages : en
Pages : 432

View

Book Description
In today’s financial market, portfolio and risk management are facing an array of challenges. This is due to increasing levels of knowledge and data that are being made available that have caused a multitude of different investment models to be explored and implemented. Professionals and researchers in this field are in need of up-to-date research that analyzes these contemporary models of practice and keeps pace with the advancements being made within financial risk modelling and portfolio control. Recent Applications of Financial Risk Modelling and Portfolio Management is a pivotal reference source that provides vital research on the use of modern data analysis as well as quantitative methods for developing successful portfolio and risk management techniques. While highlighting topics such as credit scoring, investment strategies, and budgeting, this publication explores diverse models for achieving investment goals as well as improving upon traditional financial modelling methods. This book is ideally designed for researchers, financial analysts, executives, practitioners, policymakers, academicians, and students seeking current research on contemporary risk management strategies in the financial sector.

Advanced Network Technologies and Intelligent Computing

Advanced Network Technologies and Intelligent Computing PDF Author: Isaac Woungang
Publisher: Springer Nature
ISBN: 3030960404
Category :
Languages : en
Pages :

View

Book Description


Machine Intelligence and Big Data Analytics for Cybersecurity Applications

Machine Intelligence and Big Data Analytics for Cybersecurity Applications PDF Author: Yassine Maleh
Publisher: Springer Nature
ISBN: 303057024X
Category : Computers
Languages : en
Pages : 539

View

Book Description
This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances on machine intelligence and big data analytics for cybersecurity applications.

Contemporary Methods in Bioinformatics and Biomedicine and Their Applications

Contemporary Methods in Bioinformatics and Biomedicine and Their Applications PDF Author: Sotir S. Sotirov
Publisher: Springer Nature
ISBN: 3030966380
Category :
Languages : en
Pages :

View

Book Description


Reinventing the Social Scientist and Humanist in the Era of Big Data

Reinventing the Social Scientist and Humanist in the Era of Big Data PDF Author: Susan Brokensha
Publisher: UJ Press
ISBN: 1928424376
Category : Social Science
Languages : en
Pages : 205

View

Book Description
This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.