Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
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.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2020
- DOI:
- arXiv:
- arXiv:2001.08317
- Bibcode:
- 2020arXiv200108317W
- Keywords:
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- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- 10 pages, 7 figures