Time Series Forecasting, Evaluation and Deployment (Time-FED)

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2021-09-01 (day precision)Source: 2021-09-01T00:00:00

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

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.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

No

Demographic features

Not reported

Training and evaluation data

time series data

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported