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Report NEP-FOR-2020-04-20
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:
- Michael Pfarrhofer, 2020, "Forecasts with Bayesian vector autoregressions under real time conditions," Papers, arXiv.org, number 2004.04984, Apr.
- Jonghyeon Min, 2020, "Financial Market Trend Forecasting and Performance Analysis Using LSTM," Papers, arXiv.org, number 2004.01502, Mar.
- Zsolt Darvas & Zoltán Schepp, 2020, "Forecasting exchange rates of major currencies with long maturity forward rates," Bruegel Working Papers, Bruegel, number 35829, Apr.
- Philip Ndikum, 2020, "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers, arXiv.org, number 2004.01504, Mar.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020, "Targeting predictors in random forest regression," Papers, arXiv.org, number 2004.01411, Apr, revised Nov 2020.
- Ioannis Boukas & Damien Ernst & Thibaut Th'eate & Adrien Bolland & Alexandre Huynen & Martin Buchwald & Christelle Wynants & Bertrand Corn'elusse, 2020, "A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding," Papers, arXiv.org, number 2004.05940, Apr.
- David F. Hendry, 2020, "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers, Economics Group, Nuffield College, University of Oxford, number 2020-W02, Feb.