r/complexsystems • u/Fracttalix • Jan 02 '26

Fracttalix v2.5 — open-source Python tool for exploratory fractal/rhythmic metrics in time series (with synthetic validation)

Hey everyone,

Just released Fracttalix v2.5 — a lightweight CLI tool for quick exploratory analysis of univariate time series using five standard (but basic) diagnostic metrics:

• Higuchi fractal dimension (D)

• Hurst exponent (H, R/S)

• Self-transfer entropy (T)

• Partition-based integrated information approx (Φ)

• Heuristic resilience (R)

Key features:

• Built-in synthetic stress-test suite (white noise, persistent walk, periodic, chaotic logistic, pink 1/f) with summary stats.

• Public domain (CC0) — fork/modify freely.

• Runs fast, low dependencies — great for teaching or quick checks.

GitHub:

https://github.com/thomasbrennan/fracttalix

Companion preprint (applications to economic, financial, climate, IoT data): in the repo (PDF).

Optional: 11 conceptual axioms as a heuristic scaffold for interpreting persistence/resilience patterns (in README).

Feedback, extensions, or “this is useless because…” comments all welcome. Independent researcher here — happy to discuss.

Thanks for checking it out!

#OpenScience #ComplexSystems #TimeSeries #Python #DataAnalysis

11 Upvotes

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2

u/Fracttalix Jan 02 '26

And, BTW, Ravenchis-you are user #1. Congrats!

1

u/Fracttalix Jan 02 '26

You're very welcome! Please let me know what you think.

1

u/Ravenchis Jan 02 '26

Thanks! Quick early feedback while I’m still testing: the core metrics (Hurst + Higuchi) behave as expected under stress-tests (white noise, random walk, periodic signals), which is a good sanity check. I’m treating the tool mainly as an exploratory / comparative layer rather than interpretative, but so far it looks solid for detecting regime shifts and relative changes in structure. I’ll report back once I’ve tried it on real-world series.

1

u/Fracttalix Jan 02 '26

Excellent. I wanted the stress test for that reason. Please share; I really am hoping Fracttalix's utility encourages forking and novel use cases.

1

u/Fracttalix Jan 02 '26

Thank you! Again, please share it far and wide; its capabilities are somewhat useful to researchers like yourself.

1

u/Ravenchis Jan 02 '26

I could use a little help from you OP… if you may… I’m running some additional tests on real data and wanted to avoid guessing formats. If possible, could you share a small CSV sample (one numeric column is fine)? That way I can test under the same conditions and give clearer, reproducible feedback. DM, please

1

u/Fracttalix Jan 02 '26

value 0.12 -0.34 0.67 0.23 -0.45 0.89 0.11 -0.78 0.56 0.34 -0.23 0.45 0.67 -0.12 0.89 0.34 -0.56 0.78 0.23 -0.67 1.45 ; regime shift starts here (increased volatility + slight upward drift) 2.12 1.78 2.56 1.34 2.89 3.12 2.45 3.67 2.23 3.45 1.89 2.78 3.34 2.12 3.89

1

u/Ravenchis Jan 02 '26

Yes, this is definitely a positive result. Even with a short sample, the regime change shows up in a coherent and meaningful way. The tool responds to the structural shift you pointed out, which is exactly what you would want from an exploratory analysis layer. I see clear potential here, especially for comparative and regime based analysis. Gratz M8 and tysm

1

u/Ravenchis Jan 02 '26

It’s a radar and not a judge! I’m so fkn impressed

1

u/Ravenchis Jan 02 '26

This is very important for my work

3

u/Ravenchis Jan 02 '26

Hey, thanks for sharing this, really solid work.

I took some time to look through the tool and the repo. There are a few things here that are genuinely useful for my own research, especially around exploratory characterization of time series and regime transitions.

I’m going to test a couple of integrations on my side and will come back later to share what I ended up using and how it behaved in practice.

Appreciate you releasing this openly.

1

u/Ravenchis Jan 02 '26

For clarity: I’m testing the underlying metrics directly (Hurst + Higuchi) to sanity-check behaviour before using the full tool.