FAQ

Where does TimeSeriesEconPy come from?

It's a Python port of TimeSeriesEcon.jl, the Bank of Canada's Julia time-series language. The primary motivation is that the BoC pipeline now spans environments where Julia isn't available (MS Fabric, Databricks); the port keeps the same vocabulary so models written against either side translate idiom-for-idiom.

The pinned upstream commit lives in tsecon._mirror.

How does it compare to pandas?

pandas is a general-purpose DataFrame library. tsecon is a time-series primitive library — it stops at TSeries / MVTSeries / Workspace and leaves higher-level analytics to downstream packages. Concretely:

• tsecon has first-class fiscal-year frequencies (Yearly(end_month=3), Quarterly(end_month=2)); pandas can express these via Q-FEB aliases but the result is less ergonomic.
• tsecon has lossless frequency conversion (fconvert) with the same method-code semantics as the Julia upstream.
• tsecon integrates with pandas (and polars) via lazy-imported converters — see tsecon.interop. You can move data back and forth without tsecon becoming a hard dependency of your pandas workflow.

How does it compare to TimeSeriesEcon.jl?

Semantics: identical wherever Python allows. The Migration from Julia page is the one-page idiom map. The Cython strategy page shows the performance picture: for non-vectorisable hot paths we Cythonise and close the gap to within microbenchmark noise; for already-vectorised paths NumPy is already at Julia's speed.

Why no 2020Q1 literal sugar?

See Frequency model.

Where do I file a bug?

GitHub Issues.

What's the license?

MIT. See LICENSE.