Report NEP-FOR-2022-09-05

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:

• Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022. "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series 561, Central Bank of Brazil, Research Department.
• Ryan Thompson & Yilin Qian & Andrey L. Vasnev, 2022. "Flexible global forecast combinations," Papers 2207.07318, arXiv.org, revised Mar 2024.
• Jacob Goldin & Julian Nyarko & Justin Young, 2022. "Forecasting Algorithms for Causal Inference with Panel Data," Papers 2208.03489, arXiv.org, revised Apr 2024.
• Shaswat Mohanty & Anirudh Vijay & Nandagopan Gopakumar, 2022. "StockBot: Using LSTMs to Predict Stock Prices," Papers 2207.06605, arXiv.org, revised Jul 2022.
• Niccol`o Ajroldi & Jacopo Diquigiovanni & Matteo Fontana & Simone Vantini, 2022. "Conformal Prediction Bands for Two-Dimensional Functional Time Series," Papers 2207.13656, arXiv.org, revised Jul 2023.
• Florian Huber & Luca Onorante & Michael Pfarrhofer, 2022. "Forecasting euro area inflation using a huge panel of survey expectations," Papers 2207.12225, arXiv.org.
• Mostafa Shabani & Dat Thanh Tran & Juho Kanniainen & Alexandros Iosifidis, 2022. "Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification," Papers 2207.11577, arXiv.org.
• Stefanos Bennett & Jase Clarkson, 2022. "Time Series Prediction under Distribution Shift using Differentiable Forgetting," Papers 2207.11486, arXiv.org.
• Gaetan Bakalli & St'ephane Guerrier & Olivier Scaillet, 2022. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Papers 2208.00972, arXiv.org.