Report NEP-FOR-2022-10-17

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:

• Hugo Inzirillo & Ludovic De Villelongue, 2022. "An Attention Free Long Short-Term Memory for Time Series Forecasting," Papers 2209.09548, arXiv.org.
• Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
• Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
• Roberto Baviera & Pietro Manzoni, 2022. "Tree-Based Learning in RNNs for Power Consumption Forecasting," Papers 2209.01378, arXiv.org.
• Yang Liu & Di Yang & Mr. Yunhui Zhao, 2022. "Housing Boom and Headline Inflation: Insights from Machine Learning," IMF Working Papers 2022/151, International Monetary Fund.