  Variational autoencoders (VAE) are powerful generative models that learn the
latent representations of input data as random variables. Recent studies show
that VAE can flexibly learn the complex temporal dynamics of time series and
achieve more promising forecasting results than deterministic models. However,
a major limitation of existing works is that they fail to jointly learn the
local patterns (e.g., seasonality and trend) and temporal dynamics of time
series for forecasting. Accordingly, we propose a novel hybrid variational
autoencoder (HyVAE) to integrate the learning of local patterns and temporal
dynamics by variational inference for time series forecasting. Experimental
results on four real-world datasets show that the proposed HyVAE achieves
better forecasting results than various counterpart methods, as well as two
HyVAE variants that only learn the local patterns or temporal dynamics of time
series, respectively.
