Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

2020-01-23 · Neo Wu, Bradley Green, Xue Ben, Shawn O'Banion

In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenza-like illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.

📄 PDF Abstract BibTeX arXiv:2001.08317

Code (5)

KasperGroesLudvigsen/influenza_transformer/blob/main/README.md pytorch

LiamMaclean216/Pytorch-Chatbot pytorch

LiamMaclean216/Pytorch-Transfomer pytorch

Schlam/LSTM-time-series-forecasting

yuyama137/influenza pytorch

Tasks

BIG-bench Machine LearningTime SeriesTime Series Analysis Time Series Forecasting

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Decision Making

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Spatial Reasoning

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Federated Learning