PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series

Understand

Real-world time series data that commonly reflect sequential human behavior are often uniquely irregularly sampled and sparse, with highly nonuniform sampling over time and entities.

• Yet, commonly-used pretraining and augmentation methods for time series are not specifically designed for such scenarios.
• In this paper, we present PAITS (Pretraining and Augmentation for Irregularly-sampled Time Series), a framework for identifying suitable pretraining strategies for sparse and irregularly sampled time series datasets.
• PAITS leverages a novel combination of NLP-inspired pretraining tasks and augmentations, and a random search to identify an effective strategy for a given dataset.