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Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
[article]
2020
arXiv
pre-print
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities may release taxi data for multiple years while others only release a few days of data; some regions may have constant water quality data monitored by sensors whereas some regions
arXiv:1901.08518v3
fatcat:7zklfbdhtrbaznovztimoac66u