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

TL;DR

This paper introduces a Transformer-based approach for time series forecasting, demonstrating its effectiveness on influenza prevalence data and showing it performs comparably to current leading methods.

Contribution

The paper presents a novel Transformer-based framework for time series forecasting that is versatile for univariate and multivariate data, applied to influenza prediction.

Findings

Comparable forecasting accuracy to state-of-the-art methods

Effective learning of complex temporal patterns

Versatile application to different types of time series

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.