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Report NEP-FOR-2026-02-16
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:
- Han Su & Xiaojia Guo & Xiaoke Zhang, 2026, "Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts," Papers, arXiv.org, number 2602.11379, Feb.
- Giovanni Bonaccolto & Massimiliano Caporin & Oguzhan Cepni & Rangan Gupta, 2026, "Forecasting Realized Volatility of State-Level Stock Markets of the United States: The Role of Sentiment," Working Papers, University of Pretoria, Department of Economics, number 202603, Feb.
- Yurui Wu & Qingying Deng & Wonou Chung & Mairui Li, 2026, "Test-Time Adaptation for Non-stationary Time Series: From Synthetic Regime Shifts to Financial Markets," Papers, arXiv.org, number 2602.00073, Jan.
- Jan Ditzen & Erkal Ersoy & Haoyang Li & Francesco Ravazzolo, 2026, "Forecasting Oil Consumption: The Statistical Review of World Energy Meets Machine Learning," Papers, arXiv.org, number 2602.01963, Feb.
- M.Jahangir Alam & Shane Boyle & Huiyu Li & Tatevik Sekhposyan, 2026, "ChatMacro: Evaluating Inflation Forecasts of Generative AI," Working Paper Series, Federal Reserve Bank of San Francisco, number 2026-04, Feb, DOI: 10.24148/wp2026-04.
- Matthew C. Johnson & Matteo Luciani & Minzhengxiong Zhang & Kenichiro McAlinn, 2026, "Predictive Synthesis under Sporadic Participation: Evidence from Inflation Density Surveys," Papers, arXiv.org, number 2602.05226, Feb.
- Oskar V{aa}le & Shiliang Zhang & Sabita Maharjan & Gro Kl{ae}boe, 2026, "Exploring the Interpretability of Forecasting Models for Energy Balancing Market," Papers, arXiv.org, number 2602.00049, Jan.
- Eurydice Fotopoulou & Iyke Maduako & M. Belen Sbrancia & Prachi Srivastava, 2026, "Nowcasting Economic Growth with Machine Learning and Satellite Data," IMF Working Papers, International Monetary Fund, number 2026/020, Jan.
- Luis Ontaneda Mijares & Nick Firoozye, 2026, "Adaptive Benign Overfitting (ABO): Overparameterized RLS for Online Learning in Non-stationary Time-series," Papers, arXiv.org, number 2601.22200, Jan.
- Yuanhong Wu & Wei Ye & Jingyan Xu & D. Frank Hsu, 2026, "Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis," Papers, arXiv.org, number 2602.00037, Jan, revised Mar 2026.
- Felipe A. Csaszar & Aticus Peterson & Daniel Wilde, 2026, "The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament," Papers, arXiv.org, number 2602.01684, Feb.
- Keywan Christian Rasekhschaffe, 2026, "Generative AI for Stock Selection," Papers, arXiv.org, number 2602.00196, Jan.
- Marcin Dec, 2026, "Extracting Risk Free Interest Rate Expectations in a Less Liquid Government Bond Markets," GRAPE Working Papers, GRAPE Group for Research in Applied Economics, number 113.
- Samir Orujov & Victor Elvira & Audrey Poterie & Farid Rajabov & Francois Septier, 2025, "VS-LTGARCHX: A Flexible Variable Selection in Log-TGARCHX Models," Post-Print, HAL, number hal-04283159, May, DOI: 10.1515/jtse-2023-0035.
- Kohei Asao & Raju Huidrom, 2026, "Understanding and Forecasting Inflation in Timor-Leste," IMF Working Papers, International Monetary Fund, number 2026/024, Feb.
- Mayoral, L. & Mueller, H. & Philipp, M. & Rauh, C. & Vassallo, R., 2026, "Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2609, Jan.
- Kevin Lee & Kalvinder Shields, 2026, "Monitoring Macroeconomic Prospects with a Meta VAR-E Dashboard," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-10, Feb.