Report NEP-FOR-2026-05-11

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:

• Filip Blaha & Jan Botka & Josef Sveda & Ales Michl, 2026, "AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights," Working Papers, Czech National Bank, Research and Statistics Department, number 2026/09, Apr.
• Max Kleinebrahm & Jonathan Berrisch & Philipp Eiser & Wolf Fichtner & Veit Hagenmeyer & Matthias Hertel & Nils Koster & Sebastian Lerch & Ralf Mikut & Jan Priesmann & Melanie Schienle & Benjamin Schae, 2026, "Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting," Papers, arXiv.org, number 2604.24705, Apr.
• My Thi Diem Phan & Trung Tuyen Truong & Hoai Phuong Ha & Dat Thanh Nguyen, 2026, "Electricity price forecasting across Norway's five bidding zones in the post-crisis era," Papers, arXiv.org, number 2604.26634, Apr.
• Yusuke Oh & Mototsugu Shintani, 2026, "Forecasting Recessions Using Machine Learning on Text Data and Mixed-Frequency Predictors," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 26-E-07, Mar.
• Alexis Lazanas & Spyridon Karpouzis, 2026, "Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling," Papers, arXiv.org, number 2604.22801, Apr, revised May 2026.
• Lin William Cong & Guanhao Feng & Jingyu He & Yuanzhi Wang, 2026, "Mosaics of Predictability," NBER Working Papers, National Bureau of Economic Research, Inc, number 35158, Apr.
• Maksym Nechepurenko & Pavel Shuvalov, 2026, "Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents," Papers, arXiv.org, number 2605.00420, May, revised May 2026.
• Sicco Kooiker & Janneke van Brummelen & Julia Schaumburg & Marcin Zamojski, 2026, "Self-driving neural networks for term structure modeling," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 26-007/III, Feb.
• Thomas Conlon & John Cotter & Iason Kynigakis, 2026, "Machine Learning Forecasts of Asymmetric Betas Using Firm-Specific Information," Papers, arXiv.org, number 2604.22933, Apr.
• Olivia Zhang & Zhilin Zhang, 2026, "A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective," Papers, arXiv.org, number 2605.05211, Apr.
• Johannes Emmerling & Paul Waidelich & Mr. Matthieu Bellon & Emanuele Massetti, 2026, "When Forecasts Meet Reality: Assessing Climate Damage Functions," IMF Working Papers, International Monetary Fund, number 2026/085, May.
• Kevin J. Lansing & Adam Hale Shapiro, 2026, "Measuring Inflation Shock Momentum," Working Paper Series, Federal Reserve Bank of San Francisco, number 2026-10, Apr, DOI: 10.24148/wp2026-10.
• Knüppel, Malte & Pavlova, Lora, 2026, "Survey design and professional forecasters: The case of uncertainty in the US SPF," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 26-017.