Zusammenfassung
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.
Nutzer