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Saturday, October 19, 2019

Attention for Advanced Forecasting and Classification

Interesting and quite technical view of forecasting and classification that is worth a look.  Of course accurate and timely forecasting is important for most businesses.  Considerable piece here, below an intro with much more at the link.  Have never seen it done accurately enough with these kinds of methods.

Attention for time series forecasting and classification
Harnessing the most recent advances in NLP for time series forecasting and classification  By Isaac Godfried

Transformers (specifically self-attention) have powered significant recent progress in NLP. 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. With their recent success in NLP one would expect widespread adaptation to problems like time series forecasting and classification. After all, both involve processing sequential data. However, to this point research on their adaptation to time series problems has remained limited. Moreover, while some results are promising, others remain more mixed. 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. This article will assume that you have a basic understanding of soft-attention, self-attention, and transformer architecture. If you don’t please read one of the linked articles. You can also watch my video from the PyData Orono presentation night.

Attention for time series data: Review

The need to accurately forecast and classify time series data spans across just about every industry and long predates machine 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. .... " 

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