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

• URL: http://arxiv.org/abs/2001.08317v1
• Date: Thu, 23 Jan 2020 00:22:22 GMT
• Title: Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
• Authors: Neo Wu, Bradley Green, Xue Ben, Shawn O'Banion
• Abstract summary: Time series data are prevalent in many scientific and engineering disciplines. We present a new approach to time series forecasting using Transformer-based machine learning models. We show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.
• Score: 2.997238772148965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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