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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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, arXiv.org, number 2209.09548, Sep.
- Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022, "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper, CPB Netherlands Bureau for Economic Policy Analysis, number 441, Oct, DOI: 10.34932/01mq-sn15.
- Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022, "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers, arXiv.org, number 2209.09649, Sep, revised Jul 2023.
- Roberto Baviera & Pietro Manzoni, 2022, "RNN(p) for Power Consumption Forecasting," Papers, arXiv.org, number 2209.01378, Sep, revised Nov 2025.
- Yang Liu & Di Yang & Mr. Yunhui Zhao, 2022, "Housing Boom and Headline Inflation: Insights from Machine Learning," IMF Working Papers, International Monetary Fund, number 2022/151, Jul.