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Irregularly sampled time series data with missing values is observed in manyfields like healthcare, astronomy, and climate science. Interpolation of thesetypes of time series is crucial for tasks such as root cause analysis andmedical diagnosis, as well as for smoothing out irregular or noisy data. Toaddress this challenge, we present a novel encoder-decoder architecture called"Tripletformer" for probabilistic interpolation of irregularly sampled timeseries with missing values. This attention-based model operates on sets ofobservations, where each element is composed of a triple of time, channel, andvalue. The encoder and decoder of the Tripletformer are designed with attentionlayers and fully connected layers, enabling the model to effectively processthe presented set elements. We evaluate the Tripletformer against a range ofbaselines on multiple real-world and synthetic datasets and show that itproduces more accurate and certain interpolations. Results indicate animprovement in negative loglikelihood error by up to 32% on real-world datasetsand 85% on synthetic datasets when using the Tripletformer compared to the nextbest model.

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series