Additionally, in many cases, they are faster than using an RNN/LSTM (particularly with some of the techniques we will discuss). In many cases, they offer overall performance improvements (other vanilla LSTMs/RNNs) with the benefit of interpretability in the form of attention heat maps. While there is no single solution to address all these issues, deep models with attention provide a compelling case. Usually, the common reasons for choosing these methods remain interpretability, limited data, ease of use, and training cost. In some cases, they may even use models like XGBoost fed with manually manufactured time intervals. Particularly, in industry many data scientists still utilize simple autoregressive models instead of deep learning. For instance, in hospitals you may want to triage patients with the highest mortality early-on and forecast patient length of stay in retail you may want to predict demand and forecast sales utility companies want to forecast power usage, etc.ĭespite the successes of deep learning with respect to computer vision many time series models are still shallow. The need to accurately forecast and classify time series data spans across just about every industry and long predates machine learning. 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. 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
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