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Report NEP-FOR-2020-01-06
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:
- Watts, Duncan J & Beck, Emorie D & Bienenstock, Elisa Jayne & Bowers, Jake & Frank, Aaron & Grubesic, Anthony & Hofman, Jake M. & Rohrer, Julia Marie & Salganik, Matthew, 2018, "Explanation, prediction, and causality: Three sides of the same coin?," OSF Preprints, Center for Open Science, number u6vz5, Oct, DOI: 10.31219/osf.io/u6vz5.
- Davide Ferrari & Francesco Ravazzolo & Joaquin L. Vespignani, 2019, "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 376, Dec, DOI: 10.24149/gwp376.
- Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019, "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES, ULB -- Universite Libre de Bruxelles, number 2019-32, Dec.
- Firuz Kamalov, 2019, "Forecasting significant stock price changes using neural networks," Papers, arXiv.org, number 1912.08791, Nov.
- Sidra Mehtab & Jaydip Sen, 2019, "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing," Papers, arXiv.org, number 1912.07700, Dec.
- Satoshi KONDO & Daisuke MIYAKAWA & Kengo SHIRAKI & Miki SUGA & Teppei USUKI, 2019, "Using Machine Learning to Detect and Forecast Accounting Fraud," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 19103, Dec.