METR-LA Point Missing

Introduced by Cini et al. in Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

The original dataset from Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting contains traffic readings collected from 207 loop detectors on highways in Los Angeles County, aggregated in 5 minutes intervals over four months between March 2012 and June 2012.

The Point missing setting, introduced in Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks, is a variant for imputation in which 25% of data are masked out uniformly at random. Results on this dataset are assumed to be obtained in-sample, meaning that the test interval is used also for training, excluding data used for evaluation.