Report NEP-FOR-2021-04-12

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Jonathan Berrisch & Florian Ziel, 2021. "CRPS Learning," Papers 2102.00968, arXiv.org, revised Nov 2021.
• Raheem, Ibrahim & Vo, Xuan Vinh, 2020. "A new approach to exchange rate forecast: The role of global financial cycle and time-varying parameters," MPRA Paper 105359, University Library of Munich, Germany.
• Rajiv Sethi & Julie Seager & Emily Cai & Daniel M. Benjamin & Fred Morstatter, 2021. "Models, Markets, and the Forecasting of Elections," Papers 2102.04936, arXiv.org, revised May 2021.
• Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Forecasting with Deep Learning: S&P 500 index," Papers 2103.14080, arXiv.org.
• Marcelo Medeiros & Alexandre Street & Davi Vallad~ao & Gabriel Vasconcelos & Eduardo Zilberman, 2020. "Short-Term Covid-19 Forecast for Latecomers," Papers 2004.07977, arXiv.org, revised Sep 2021.
• Virk, Nader & Javed, Farrukh & Awartani, Basel, 2021. "A reality check on the GARCH-MIDAS volatility models," Working Papers 2021:2, Örebro University, School of Business.
• Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
• Raheem, Ibrahim, 2020. "Global financial cycles and exchange rate forecast: A factor analysis," MPRA Paper 105358, University Library of Munich, Germany.
• Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
• Jaydip Sen & Sidra Mehtab, 2021. "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers 2103.15096, arXiv.org.
• Pratyush Muthukumar & Jie Zhong, 2021. "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers 2102.01290, arXiv.org.