You can also watch my video from the PyData Orono presentation night.
If you don’t please read one of the linked articles. This article will assume that you have a basic understanding of soft-attention, self-attention, and transformer architecture.
In this article, I will review current literature on applying transformers as well as attention more broadly to time series problems, discuss the current barriers/limitations, and brainstorm possible solutions to (hopefully) enable these models to achieve the same level success as in NLP. Moreover, while some results are promising, others remain more mixed.
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.
Series prediction? If so, how to I get them? Are there in Keras? It is also recommended to use a smaller learning rate on the second run in order to adapt it gradually to the new data.Īre there any pre-trained model (LSTM, RNN, or any other ANN) for time To do this, you simply start training from a loaded state instead of random initialization and save the model afterwards. But keep in mind that if two datasets represent very different populations, the network will soon "forget" what it learned on the first run and will optimize to the second one. In general, it's called transfer learning. When you do it in batches you have all the data in one moment. Will it be possible to continue training the model then? It is not the same thing as training it in batches.
Suppose that in a month, I will have access to another dataset (corresponding to same data or similar data, in the future possibly, but not exclusively). Suppose I have a dataset now and I use it to train my model. I mean it would be super useful if there a website containing pre trained models, so that people wouldn't have to speent too much time training them.ġ. So I was wondering: since you can save models in keras are there any pre-trained model (LSTM, RNN, or any other ANN) for time series prediction? If so, how to I get them? Are there in Keras? I tried with ANN and LSTM, played around a lot with the various parameters, but all I could get was 8% better than the persistence prediction. I am trying to solve a time series prediction problem.