Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
This work addresses forecasting problems in domains like public health, but it is incremental as it applies an existing model type to a specific case.
The paper tackles time series forecasting by developing a Transformer-based method that learns complex patterns using self-attention, and demonstrates its effectiveness by achieving results favorably comparable to state-of-the-art in influenza prevalence forecasting.
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.