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

By Neo Wu, Bradley Green, Xue Ben, Shawn O'Banion. arXiv.org, 2020

573 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Epidemiology

View Paper: https://www.semanticscholar.org/paper/f5a28db512357b700b62fb655ef4a90864e2fe7e

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Citations in the corpus

Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency (2022, background): “Hence, pre-training models in CV [36, 37, 14] and NLP [10, 15, 38] are not directly applicable due to data modality mismatch, and the existing results leave room for improvement [31, 39, 40].”
Transformers in Time-Series Analysis: A Tutorial (2022, methodology): “Many forecasting Transformers for time-series data have been developed in the recent literature [70, 72, 73, 74, 82, 66, 67, 81, 78, 64, 75].”
Self-Supervised Representation Learning: Introduction, advances, and challenges (2021, unknown): “In terms of time-series forecasting, transformers based on sequential masked prediction pretext have significantly out-performed conventional autoregressive models in predicting disease transmission [88].”
PanGu-Coder: Program Synthesis with Function-Level Language Modeling (2022, background): “Lately, such models have been adapted to more speciﬁc language domains, e.g. to the Biomedical [37, 46, 9], Legal [14], Cyber Security [2], and Finance [6] domains, while simultaneously expanding to include signals from modalities other than natural language, e.g. Vision [65, 18, 15, 13, 64, 25], Pr”