Printed from https://ideas.repec.org/n/nep-for/2020-09-07.html
Report NEP-FOR-2020-09-07
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:
- Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020, "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 29/20.
- Jorge Fornero & Andrés Gatty, 2020, "Back testing fan charts of activity and inflation: the Chilean case," Working Papers Central Bank of Chile, Central Bank of Chile, number 881, Jun.
- Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020, "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers, arXiv.org, number 2008.08004, Aug, revised Dec 2020.
- Boyuan Zhang, 2020, "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers, arXiv.org, number 2007.02435, Jul, revised Oct 2020.
- Grzegorz Marcjasz & Jesus Lago & Rafa{l} Weron, 2020, "Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs," Papers, arXiv.org, number 2008.08006, Aug.
- Jan Wessel, 2020, "Using weather forecasts to forecast whether bikes are used," Working Papers, Institute of Transport Economics, University of Muenster, number 32, Jun.
- Kaukin, Andrei (Каукин, Андрей) & Kosarev, Vladimir (Косарев, Владимир), 2020, "Identification and forecasting of the phases of the Russian economic cycle, taking into account the sectoral structure of the economy using artificial neural networks
[Идентификация И Прогнозирование Фаз Российского Экономического Цикла С Учетом О," Working Papers, Russian Presidential Academy of National Economy and Public Administration, number 052019, May. - Hannes Mueller & Christopher Rauh, 2019, "The Hard Problem of Prediction for Conflict Prevention," Cahiers de recherche, Centre interuniversitaire de recherche en économie quantitative, CIREQ, number 02-2019, Apr.
- Paulina Concha Larrauri & Upmanu Lall, 2020, "Big Data links from Climate to Commodity Production Forecasts and Risk Management," Papers, arXiv.org, number 2007.03015, Jul.
- Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020, "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers, arXiv.org, number 2008.07564, Aug.
- Daniel v{S}tifani'c & Jelena Musulin & Adrijana Miov{c}evi'c & Sandi Baressi v{S}egota & Roman v{S}ubi'c & Zlatan Car, 2020, "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory," Papers, arXiv.org, number 2007.02673, Jul.
- Nathaniel Tomasetti & Catherine Forbes & Anastasios Panagiotelis, 2020, "Updating Variational Bayes: Fast Sequential Posterior Inference," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 27/20.