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Inspired by the\nphilosophy of Physics-Informed Machine Learning (PIML), the\nproposed framework incorporates information from classical\nstatistical models into neural network learning, creating a\nhybrid forecasting approach that combines domain knowledge with\ndata-driven intelligence. The methodology is motivated by\nhybrid forecasting framework proposed by Yeasin and Paul (2024)\n.", "License": "GPL-3", "Encoding": "UTF-8", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-17 15:43:51 UTC", "User": "root" }, "Author": "Md Yeasin [aut], Ranjit Kumar Paul [aut, cre], Pushkar Bora\n[aut]", "Maintainer": "Ranjit Kumar Paul ", "Repository": "https://ranjitstat.r-universe.dev", "Date/Publication": "2026-09-16 11:40:02 UTC", "RemoteUrl": "https://github.com/cran/ARInfoLSTM", "RemoteRef": "HEAD", "RemoteSha": "c0b31063f285d42fa89a1976f519ad0a28973f52", "_user": "ranjitstat", "_type": "src", "_file": "ARInfoLSTM_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/fb80108b6bf56dceb04987d87f7122e72fd1ed8c11abb803c2b0934590cd094c", "_filesize": 118306, "_sha256": "fb80108b6bf56dceb04987d87f7122e72fd1ed8c11abb803c2b0934590cd094c", "_expires": "2026-12-26T15:47:17.000Z", "_created": 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