ArXiv Preprint
Asynchronous time series are often observed in several applications such as
health care, astronomy, and climate science, and pose a significant challenge
to the standard deep learning architectures. Interpolation of asynchronous time
series is vital for many real-world tasks like root cause analysis, and medical
diagnosis. In this paper, we propose a novel encoder-decoder architecture
called Tripletformer, which works on the set of observations where each set
element is a triple of time, channel, and value, for the probabilistic
interpolation of the asynchronous time series. Both the encoder and the decoder
of the Tripletformer are modeled using attention layers and fully connected
layers and are invariant to the order in which set elements are presented. The
proposed Tripletformer is compared with a range of baselines over multiple
real-world and synthetic asynchronous time series datasets, and the
experimental results attest that it produces more accurate and certain
interpolations. We observe an improvement in negative loglikelihood error up to
33% over real and 800% over synthetic asynchronous time series datasets
compared to the state-of-the-art model using the Tripletformer.
Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-thieme
2022-10-05
