{"paper":{"title":"Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Nested sampling computes Bayesian evidence to select optimal ARIMA orders for astronomical time series.","cross_cats":["astro-ph.EP","astro-ph.SR"],"primary_cat":"astro-ph.IM","authors_text":"Ajinkya Naik, Will Handley","submitted_at":"2025-12-01T17:45:00Z","abstract_excerpt":"The era of large-scale, high-cadence astronomical surveys demands efficient and robust methods for time-series analysis. ARIMA models provide a versatile parametric description of stochastic variability in this context. However, their practical use is limited by the challenge of selecting optimal model orders while avoiding overfitting. We present a novel solution this problem by combining Autoregressive Integrated Moving Average (ARIMA) models with the Nested Sampling algorithm. Our method yields Bayesian evidences for model comparison and also incorporates an intrinsic Occam's penalty for un"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results demonstrate that nested sampling offers a rigorous and computationally tractable alternative to autoregressive model selection in astronomical time-series analysis.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That astronomical time series are adequately described by linear ARIMA processes whose orders can be reliably distinguished by Bayesian evidence computed via nested sampling, without significant model misspecification or sampling failures in the high-dimensional parameter spaces of higher-order models.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Nested sampling applied to ARIMA models enables Bayesian order selection and parameter inference that recovers ground truth in simulations and fits stochastic variability in sunspot, Kepler, and TESS light curves.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Nested sampling computes Bayesian evidence to select optimal ARIMA orders for astronomical time series.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"74ca570039b174c5283af148b72bb376984c278307556dae7687c73539120883"},"source":{"id":"2512.01929","kind":"arxiv","version":3},"verdict":{"id":"1571b40e-d0e6-460f-9731-aebf93494e27","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T02:18:13.062945Z","strongest_claim":"Our results demonstrate that nested sampling offers a rigorous and computationally tractable alternative to autoregressive model selection in astronomical time-series analysis.","one_line_summary":"Nested sampling applied to ARIMA models enables Bayesian order selection and parameter inference that recovers ground truth in simulations and fits stochastic variability in sunspot, Kepler, and TESS light curves.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That astronomical time series are adequately described by linear ARIMA processes whose orders can be reliably distinguished by Bayesian evidence computed via nested sampling, without significant model misspecification or sampling failures in the high-dimensional parameter spaces of higher-order models.","pith_extraction_headline":"Nested sampling computes Bayesian evidence to select optimal ARIMA orders for astronomical time series."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2512.01929/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d5321a125bf752521e920e985b5010299cb461727817b1d9c534ae29c0c77a56"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}