Report NEP-FOR-2024-07-15

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:

• Francesco Audrino & Jonathan Chassot, 2024. "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers 2406.08041, arXiv.org.
• Milen Arro-Cannarsa & Rolf Scheufele, 2024. "Nowcasting GDP: what are the gains from machine learning algorithms?," Working Papers 2024-06, Swiss National Bank.
• Barrio Castro, Tomás del & Escribano, Álvaro & Sibbertsen, Philipp, 2024. "Modeling and Forecasting the Long Memory of Cyclical Trends in Paleoclimate Data," UC3M Working papers. Economics 43987, Universidad Carlos III de Madrid. Departamento de Economía.
• Bivas Dinda, 2024. "Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy," Papers 2406.02604, arXiv.org.
• Seulki Chung, 2024. "Modelling and Forecasting Energy Market Volatility Using GARCH and Machine Learning Approach," Papers 2405.19849, arXiv.org.