probsys/AutoGP.jl

Automated Bayesian model discovery for time series data

42
/ 100
Emerging

This helps scientists, engineers, or data analysts automatically understand patterns in their time series data. You input a sequence of measurements over time, and it tells you what underlying trends, periodic cycles, or smooth changes are present, without you having to guess. This is for anyone who needs to interpret or forecast time-dependent observations.

Use this if you have a single stream of time-ordered data and need to automatically identify its core components, like seasonality or long-term trends, without manual specification.

Not ideal if you already know the exact mathematical form of your time series patterns or if you are working with multiple, interrelated time series.

time-series-analysis forecasting pattern-recognition data-interpretation signal-processing
No Package No Dependents
Maintenance 10 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

86

Forks

4

Language

Julia

License

Apache-2.0

Last pushed

Mar 03, 2026

Commits (30d)

0

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