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