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Report NEP-FOR-2025-01-20
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:
- McCloskey, PJ & Malheiros Remor, Rodrigo, 2024, "Comparative Analysis of ARIMA, VAR, and Linear Regression Models for UAE GDP Forecasting," MPRA Paper, University Library of Munich, Germany, number 122860, Sep, revised 01 Dec 2024.
- Xiaoqian Wang & Rob J Hyndman & Shanika Wickramasuriya, 2024, "Optimal Forecast Reconciliation with Time Series Selection," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 5/24.
- Xiaoqian Wang & Rob J Hyndman, 2024, "Online Conformal Inference for Multi-Step Time Series Forecasting," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 20/24.
- Paul Ghelasi & Florian Ziel, 2025, "A data-driven merit order: Learning a fundamental electricity price model," Papers, arXiv.org, number 2501.02963, Jan, revised Nov 2025.
- Yangzhuoran Fin Yang & Rob J Hyndman & George Athanasopoulos & Anastasios Panagiotelis, 2024, "Forecast Linear AugmentedProjection (FLAP): A Free Lunch to Reduce Forecast Error Variance," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 13/24.
- Meg Tulloch, , "Nowcasting and Forecasting Average Weekly Earnings in the United Kingdom," National Institute of Economic and Social Research (NIESR) Discussion Papers, National Institute of Economic and Social Research, number 565.
- Nuwani K Palihawadana & Rob J Hyndman & Xiaoqian Wang, 2024, "Sparse Multiple Index Modelsfor High-dimensional Nonparametric Forecasting," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 16/24.
- Chang, Jinyuan & Fang, Qin & Qiao, Xinghao & Yao, Qiwei, 2024, "On the Modeling and Prediction of High-Dimensional Functional Time Series," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 125599, Nov.
- Guhan Sivakumar, 2025, "HMM-LSTM Fusion Model for Economic Forecasting," Papers, arXiv.org, number 2501.02002, Jan.
- Jairo Flores & Bruno Gonzaga & Walter Ruelas-Huanca & Juan Tang, 2024, "Nowcasting Peruvian GDP with Machine Learning Methods," Working Papers, Banco Central de Reserva del Perú, number 2024-019, Dec.
- Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024, "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper, University Library of Munich, Germany, number 122899, Oct.
- Ignacio Garr'on & Andrey Ramos, 2025, "High-frequency Density Nowcasts of U.S. State-Level Carbon Dioxide Emissions," Papers, arXiv.org, number 2501.03380, Jan.