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# general
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Hi @Syed Umair Hassan! I think it is still an open question, and probably there is no definitive answer. It would probably depend more on the characteristics of the particular datasets. In general, yes, with more timestamps (either due to length or number of time series) global models tend to perform better on average than local models. Additionally, with short time series, models would probably struggle to produce accurate forecasts, as the
input_size
would be also short.
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We are currently doing research on the characteristics of datasets that favor transfer learning and global models, we will share our findings soon.
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It would be awesome if you can share your findings as well 🙂
s
Well, I am just a newbie compared to you guys =D. Have just started doing time series. I am researching on wind speed forecasting and wanted to research on using global models in this area. Also, wanted to check how transfer learning works. Have just started , would be glad to share my results when I get some. Thanks alot for your guidance.