{"paper":{"title":"MF-toolkit: A High-Performance Python Library for Multifractal Analysis with Automated Crossover Detection, Source Identification and Application to Gravitational Waves Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MF-toolkit automates crossover detection and surrogate testing to identify multifractality sources in time series.","cross_cats":["gr-qc"],"primary_cat":"cond-mat.stat-mech","authors_text":"Maria Cristina Mariani Maria Pia Beccar-Varela, Nahuel Mendez, Osei Tweneboah, Sebastian Jaroszewicz","submitted_at":"2026-04-17T17:15:43Z","abstract_excerpt":"Multifractal Detrended Fluctuation Analysis (MFDFA) is a powerful and widely used technique for characterizing the scaling properties and long-range correlations of complex time series. However, its application often involves significant practical challenges, such as the subjective identification of scaling regions (crossovers) and the disambiguation of the physical origins of multifractality. We introduce MF-toolkit, a high-performance, parallelized Python library designed to address these challenges. It integrates three key innovations: (1) fully automatic crossover detection algorithms (CDV"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We introduce MF-toolkit, a high-performance, parallelized Python library designed to address these challenges. It integrates three key innovations: (1) fully automatic crossover detection algorithms (CDV-A and SPIC), which remove operator bias and enhance reproducibility; (2) a built-in implementation of the Iterative Amplitude Adjusted Fourier Transform (IAAFT) for generating surrogate data, enabling the robust identification of the source of multifractality; and (3) a comprehensive suite for generating synthetic time series for rigorous validation.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the new automatic crossover detection algorithms (CDV-A and SPIC) accurately and generally identify true scaling regions without introducing their own bias, and that this holds for non-stationary noise in real gravitational wave data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MF-toolkit is a new Python library with automated crossover detection algorithms and surrogate analysis for multifractal time series, demonstrated on LIGO gravitational wave data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MF-toolkit automates crossover detection and surrogate testing to identify multifractality sources in time series.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c31f5bd1f1803c623c68fed83fe73357ded621982518355a9e5d4c67b3d06a00"},"source":{"id":"2604.16257","kind":"arxiv","version":1},"verdict":{"id":"ccfb74b9-e1a5-4ef7-bceb-5b252b0384d5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T06:54:49.764148Z","strongest_claim":"We introduce MF-toolkit, a high-performance, parallelized Python library designed to address these challenges. It integrates three key innovations: (1) fully automatic crossover detection algorithms (CDV-A and SPIC), which remove operator bias and enhance reproducibility; (2) a built-in implementation of the Iterative Amplitude Adjusted Fourier Transform (IAAFT) for generating surrogate data, enabling the robust identification of the source of multifractality; and (3) a comprehensive suite for generating synthetic time series for rigorous validation.","one_line_summary":"MF-toolkit is a new Python library with automated crossover detection algorithms and surrogate analysis for multifractal time series, demonstrated on LIGO gravitational wave data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the new automatic crossover detection algorithms (CDV-A and SPIC) accurately and generally identify true scaling regions without introducing their own bias, and that this holds for non-stationary noise in real gravitational wave data.","pith_extraction_headline":"MF-toolkit automates crossover detection and surrogate testing to identify multifractality sources in time series."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.16257/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":28,"sample":[{"doi":"","year":2005,"title":"Long correlations and truncated levy walks applied to the study latin-american market in- dices","work_id":"5c660238-186f-4bd0-b070-afb78b45f0a1","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1994,"title":"Mosaic organization of dna nucleotides","work_id":"eb2ac3ea-c519-4511-9980-4bf546db39b8","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Scaling detection in extrachromosomal dna","work_id":"bbe6b8e6-6e50-417b-a002-f5b11da40dad","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Characterising the interplay of dynamics and artefacts: a multifractal analysis of historical humpback whale recordings","work_id":"49bba8b5-ead0-4f33-8201-846ece813637","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"Wavelet-based multifractal analysis of the el niño/southern oscillation, the indian ocean dipole and the north atlantic oscillation","work_id":"81d8ec98-4736-48d5-b0b4-c0d92f49f3b3","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":28,"snapshot_sha256":"3d24fbcdf30ecb6bf959dd05a9c2398780e7f14a1cf1fb541156b359e6f82403","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}