Compression Scaling Law (csl) – Detecting Hidden Structure in Time Series
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The Compression Scaling Law (CSL) is a new method for detecting hidden structure in time series data, with a GitHub repository available for exploration; however, the lack of discussion suggests a need for more context or explanation.
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I’ve released a small open-source project introducing the Compression Scaling Law (CSL): A method for detecting hidden order in time series using compression and surrogate tests.
Idea:
Slice the series into windows
Quantize + losslessly compress
Compare to spectrum-preserving surrogates (IAAFT)
If real data compresses better, and that advantage scales with window size as a power law,
The slope (α) becomes a hidden structure index.
Why it matters: CSL acts as a guardrail. It flags when systems are unstable (forecasts or controls likely to fail) rather than predicting exact events.
Applications so far:
Finance (BTC, VIX, spreads)
Climate (ENSO, sunspots, rainfall)
Risk governance (adaptive retraining, anomaly gating) https://github.com/Jorus120/Compression-Scale-Law Repo (MIT license): Includes methods PDF, plain-language explainer, toy data, and compact guide.
Would love feedback from the HN community:
Where would you apply this?
Are there domains where surrogate-based compression might reveal structure we usually miss?