Report NEP-FOR-2022-09-12

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:

• Filippo Gusella & Giorgio Ricchiuti, 2022. "A State-Space Approach for Time-Series Prediction of an Heterogeneous Agent Model," Working Papers - Economics wp2022_20.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
• Yoo, Do-il, 2022. "Livestock Price Forecasting using Long Short-Term Memory Units: the Case of African Swine Fever and the COVID-19 Pandemic," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322610, Agricultural and Applied Economics Association.
• Chellai, Fatih, 2022. "Forecasting using Fuzzy Time Series," MPRA Paper 113848, University Library of Munich, Germany.
• Caio Almeida & Jianqing Fan & Gustavo Freire & Francesca Tang, 2022. "Can a Machine Correct Option Pricing Models?," Working Papers 2022-9, Princeton University. Economics Department..
• Matteo Barigozzi & Giuseppe Cavaliere & Graziano Moramarco, 2022. "Factor Network Autoregressions," Papers 2208.02925, arXiv.org, revised Feb 2024.
• Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
• Tashreef Muhammad & Anika Bintee Aftab & Md. Mainul Ahsan & Maishameem Meherin Muhu & Muhammad Ibrahim & Shahidul Islam Khan & Mohammad Shafiul Alam, 2022. "Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market," Papers 2208.08300, arXiv.org.