{ "_id": "6ad47a4b8b78ae42bccc51c8", "Package": "sufficientForecasting", "Type": "Package", "Title": "Sufficient Forecasting using Factor Models", "Version": "0.1.0", "Authors@R": "c(person(given = \"Jianqing\", \nfamily = \"Fan\",\nrole = \"aut\"),\nperson(given = \"Jing\",\nfamily = \"Fu\",\nemail = \"jingfu991224@outlook.com\",\nrole = c(\"aut\",\"cre\")),\nperson(given = \"Wei\",\nfamily = \"Luo\",\nrole = \"aut\"),\nperson(given = \"Lingzhou\",\nfamily = \"Xue\",\nrole = \"aut\"),\nperson(given = \"Jiawei\",\nfamily = \"Yao\",\nrole = \"aut\"),\nperson(given = \"Xiufan\",\nfamily = \"Yu\",\nrole = \"aut\")\n)", "Description": "The sufficient forecasting (SF) method is implemented by\nthis package for a single time series forecasting using many\npredictors and a possibly nonlinear forecasting function.\nAssuming that the predictors are driven by some latent factors,\nthe SF first conducts factor analysis and then performs\nsufficient dimension reduction on the estimated factors to\nderive predictive indices for forecasting. The package\nimplements several dimension reduction approaches, including\nprincipal components (PC), sliced inverse regression (SIR), and\ndirectional regression (DR). Methods for dimension reduction\nare as described in: Fan, J., Xue, L. and Yao, J. (2017)\n, Luo, W., Xue, L., Yao, J.\nand Yu, X. (2022) and Yu, X., Yao,\nJ. and Xue, L. 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