#TIMENET LEARNING UPDATE#

However, to this point research on their adaptation to time series problems has remained limited. After all, both involve processing sequential data.

With their recent success in NLP one would expect widespread adaptation to problems like time series forecasting and classification.

They have enabled models like BERT, GPT-2, and XLNet to form powerful language models that can be used to generate text, translate text, answer questions, classify documents, summarize text, and much more. Transformers (specifically self-attention) have powered significant recent progress in NLP. I haven’t gotten around to writing another article on this subject but you can find implementations of the transformer and several other models with attention in the flow-forecast repository.

#TIMENET LEARNING UPDATE#

Update : A lot of people have asked for an update.The article contains the most recent and up-to-date results including articles that will appear at Neurips 2019 and preprints that came out just this past August. Note this article was published on 10/18/19 I have no idea why Medium is saying that it is from 4/9/19.Harnessing the most recent advances in NLP for time series forecasting and classification

Timenet learning