{"paper":{"title":"Parametrizations, weights, and optimal prediction: Part 1","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Azzouz Dermoune, Jabrane Moustaaid, Khalifa Es-Sebaiy, Mohammed Es.Sebaiy","submitted_at":"2018-01-13T23:25:55Z","abstract_excerpt":"We consider the problem of the annual mean temperature prediction. The years taken into account and the corresponding annual mean temperatures are denoted by $0,\\ldots, n$ and $t_0$, $\\ldots$, $t_n$, respectively. We propose to predict the temperature $t_{n+1}$ using the data $t_0$, $\\ldots$, $t_n$. For each $0\\leq l\\leq n$ and each parametrization $\\Theta^{(l)}$ of the Euclidean space $\\mathbb{R}^{l+1}$ we construct a list of weights for the data $\\{t_0,\\ldots, t_l\\}$ based on the rows of $\\Theta^{(l)}$ which are correlated with the constant trend. Using these weights we define a list of pred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06533","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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"}