Time Series Forecasting, Evaluation and Deployment (Time-FED)
Description
Time-FED is a machine learning system for Time series Forecasting, Evaluation and Deployment. TimeFED was created in response to the following data realities: 1) data contains significant gaps (sometimes on the order of months or years) due to sensor outages, 2) data are not sampled at uniform rates, 3) time series data can be in stream or track form. JPL has built an infrastructure for time series prediction and forecasting that respects these realities.
Detailed example
Time-FED outputs both predictions and forecasts. Because Time-FED has been applied to many problems related to extreme events and transient science, Time-FED also finds novel or anomalous events.
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
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Time-FED is a machine learning system for Time series Forecasting, Evaluation and Deployment. TimeFED was created in response to the following data realities: 1) data contains significant gaps (sometimes on the order of months or years) due to sensor outages, 2) data are not sampled at uniform rates, 3) time series data can be in stream or track form. JPL has built an infrastructure for time series prediction and forecasting that respects these realities.
Controls / human review
ATO: No; PIA: Not published
Data needed
time series data