{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lasso-regularization-paths-for-narmax-models","title":"Lasso Regularization Paths for NARMAX Models via Coordinate Descent","arxiv_id":"1710.00598","date":"2017-10-02","proceeding":null,"authors":["Antônio H. Ribeiro","Luis A. Aguirre"],"abstract":"We propose a new algorithm for estimating NARMAX models with $L_1$\nregularization for models represented as a linear combination of basis\nfunctions. Due to the $L_1$-norm penalty the Lasso estimation tends to produce\nsome coefficients that are exactly zero and hence gives interpretable models.\nThe novelty of the contribution is the inclusion of error regressors in the\nLasso estimation (which yields a nonlinear regression problem). The proposed\nalgorithm uses cyclical coordinate descent to compute the parameters of the\nNARMAX models for the entire regularization path. It deals with the error terms\nby updating the regressor matrix along with the parameter vector. In\ncomparative timings we find that the modification does not reduce the\ncomputational efficiency of the original algorithm and can provide the most\nimportant regressors in very few inexpensive iterations. The method is\nillustrated for linear and polynomial models by means of two examples.","url_abs":"http://arxiv.org/abs/1710.00598v2","url_pdf":"http://arxiv.org/pdf/1710.00598v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lasso-regularization-paths-for-narmax-models","repo_url":"https://github.com/antonior92/NarmaxLasso.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}