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

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arxiv 2001.08317 v1 pith:QHFERS3H submitted 2020-01-23 cs.LG stat.ML

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

Neo Wu , Bradley Green , Xue Ben , Shawn O'Banion This is my paper
classification cs.LG stat.ML
keywords seriestimedataforecastingapproachcaselearningmachine
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 9 Pith papers

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