OMB Individually Reported
Data-driven approaches to filling missing time-series data within the San Francisco Bay-Delta [2024 INV#WO0000000107683; INV#WO0000000107488]
Low riskExact public inventory row
Description
Environmental time-series data may suffer from gaps at a variety of time scales, significantly reducing the number of observations to understand phenomena, identify change, calibrate models, and predict future behavior.
Detailed example
complete time-series data
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
filling in missing time-series data
Controls / human review
ATO: No; PIA: Not published