Report NEP-FOR-2021-07-26

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:

• Juyong Lee & Youngsang Cho, 2021. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Papers 2107.06174, arXiv.org.
• Angelo Garangau Menezes & Saulo Martiello Mastelini, 2021. "MegazordNet: combining statistical and machine learning standpoints for time series forecasting," Papers 2107.01017, arXiv.org.
• Naftali Cohen & Srijan Sood & Zhen Zeng & Tucker Balch & Manuela Veloso, 2021. "Visual Time Series Forecasting: An Image-driven Approach," Papers 2107.01273, arXiv.org, revised Nov 2021.
• Peter Tankov & Laura Tinsi, 2021. "Decision making with dynamic probabilistic forecasts," Papers 2106.16047, arXiv.org.
• Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
• Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021. "Stock price prediction using BERT and GAN," Papers 2107.09055, arXiv.org.
• Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Working Papers 03/2021, IMT School for Advanced Studies Lucca, revised Jul 2021.