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.
@article{arxiv.2001.08317,
title = {Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case},
author = {Neo Wu and Bradley Green and Xue Ben and Shawn O'Banion},
journal= {arXiv preprint arXiv:2001.08317},
year = {2020}
}