{ "_id": "6a1eb4f5b25058d4daadd7d8", "Package": "uotm", "Type": "Package", "Title": "Uncertainty of Time Series Model Selection Methods", "Version": "0.1.6", "Authors@R": "c(person(\"Heming Deng\", \"Developer\", role = c(\"aut\", \"cre\", \"cph\"), email = \"dheming@ruc.edu.cn\"), person(\"Sunan Gao\", \"Dev\", role = \"aut\"))", "Maintainer": "Heming Deng Developer ", "Description": "We propose a new procedure, called model uncertainty\nvariance, which can quantify the uncertainty of model selection\non Autoregressive Moving Average models. The model uncertainty\nvariance not pay attention to the accuracy of prediction, but\nfocus on model selection uncertainty and providing more\ninformation of the model selection results. And to estimate the\nmodel measures, we propose an simplify and faster algorithm\nbased on bootstrap method, which is proven to be effective and\nfeasible by Monte-Carlo simulation. At the same time, we also\nmade some optimizations and adjustments to the Model Confidence\nBounds algorithm, so that it can be applied to the time series\nmodel selection method. The consistency of the algorithm result\nis also verified by Monte-Carlo simulation. We propose a new\nprocedure, called model uncertainty variance, which can\nquantify the uncertainty of model selection on Autoregressive\nMoving Average models. The model uncertainty variance focuses\non model selection uncertainty and providing more information\nof the model selection results. To estimate the model\nuncertainty variance, we propose an simplified and faster\nalgorithm based on bootstrap method, which is proven to be\neffective and feasible by Monte-Carlo simulation. At the same\ntime, we also made some optimizations and adjustments to the\nModel Confidence Bounds algorithm, so that it can be applied to\nthe time series model selection method. The consistency of the\nalgorithm result is also verified by Monte-Carlo simulation.\nPlease see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019)\nModel Confidence Bounds for Variable Selection. Biometrics,\n75:392-403. for more information.", "License": "GPL-3", "Encoding": "UTF-8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 10:36:55 UTC", "User": "root" }, "Author": "Heming Deng Developer [aut, cre, cph], Sunan Gao Dev [aut]", "Repository": "https://heming0425.r-universe.dev", "Date/Publication": "2023-01-09 09:50:02 UTC", "RemoteUrl": "https://github.com/cran/uotm", "RemoteRef": "HEAD", "RemoteSha": "1b16055a842e2391379f8dc078d00fe65fc081a0", "MD5sum": "7a137307ababaedeaa378a9c37a45787", "_user": "heming0425", "_type": "src", "_file": "uotm_0.1.6.tar.gz", "_fileid": "a3aecfac24ffa8c2bff23f92b8236012a68a9274b3cd9efef09f30d5f4b99fac", "_filesize": 90420, "_sha256": "a3aecfac24ffa8c2bff23f92b8236012a68a9274b3cd9efef09f30d5f4b99fac", "_created": "2026-06-02T10:36:55.000Z", "_published": "2026-06-02T10:48:21.401Z", "_distro": "noble", "_jobs": [ { "job": 79052106809, "time": 122, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": 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