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Report NEP-FOR-2021-04-19
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:
- Kadir Özen & Dilem Yıldırım, 2021, "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers, ERC - Economic Research Center, Middle East Technical University, number 2101, Apr, revised Apr 2021.
- Giuseppe Storti & Chao Wang, 2021, "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers, arXiv.org, number 2104.04918, Apr, revised Jul 2021.
- Andreas Joseph & Eleni Kalamara & George Kapetanios & Galina Potjagailo & Chiranjit Chakraborty, 2021, "Forecasting UK inflation bottom up," Bank of England working papers, Bank of England, number 915, Mar.
- Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021, "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/06, Apr.
- Florian, Huber & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021, "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs," JRC Working Papers in Economics and Finance, Joint Research Centre, European Commission, number 2021-01, Mar.
- Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021, "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print, HAL, number hal-03184841.
- Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021, "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers, arXiv.org, number 2104.06259, Apr.
- Yiqi Zhao & Matloob Khushi, 2021, "Wavelet Denoised-ResNet CNN and LightGBM Method to Predict Forex Rate of Change," Papers, arXiv.org, number 2102.04861, Jan.