%0 Generic %1 wu2020transformer %A Wu, Neo %A Green, Bradley %A Ben, Xue %A O'Banion, Shawn %D 2020 %K deeplearning timeseries todo:read transformer %T Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case %U http://arxiv.org/abs/2001.08317 %X 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.

@misc{wu2020transformer, abstract = {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.}, added-at = {2021-04-28T08:19:04.000+0200}, author = {Wu, Neo and Green, Bradley and Ben, Xue and O'Banion, Shawn}, biburl = {https://www.bibsonomy.org/bibtex/24a3943735d80f2993dbfa12893a0bf63/annakrause}, description = {Deep Transformer Models for Time Series Forecasting:The Influenza Prevalence Case - 2001.08317.pdf}, interhash = {d401ed302fafa94adc07bf47aae1066d}, intrahash = {4a3943735d80f2993dbfa12893a0bf63}, keywords = {deeplearning timeseries todo:read transformer}, note = {cite arxiv:2001.08317Comment: 10 pages, 7 figures}, timestamp = {2021-04-28T08:19:04.000+0200}, title = {Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case}, url = {http://arxiv.org/abs/2001.08317}, year = 2020 }