{"id":16162542,"url":"https://github.com/sunsided/arima","last_synced_at":"2026-03-02T04:01:53.384Z","repository":{"id":141992856,"uuid":"517092781","full_name":"sunsided/arima","owner":"sunsided","description":"Experiments on AR(p)/MA(q) processes in MATLAB.","archived":false,"fork":false,"pushed_at":"2023-08-26T13:33:41.000Z","size":237,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-04T17:56:55.863Z","etag":null,"topics":["arima","arima-model","artificial-intelligence","matlab","signal-processing","statistics"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sunsided.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-07-23T15:21:43.000Z","updated_at":"2023-06-17T14:12:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"a56e9669-0f34-4238-9f15-797e748e2abe","html_url":"https://github.com/sunsided/arima","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sunsided/arima","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sunsided%2Farima","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sunsided%2Farima/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sunsided%2Farima/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sunsided%2Farima/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sunsided","download_url":"https://codeload.github.com/sunsided/arima/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sunsided%2Farima/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29992286,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-02T01:47:34.672Z","status":"online","status_checked_at":"2026-03-02T02:00:07.342Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["arima","arima-model","artificial-intelligence","matlab","signal-processing","statistics"],"created_at":"2024-10-10T02:30:41.589Z","updated_at":"2026-03-02T04:01:53.363Z","avatar_url":"https://github.com/sunsided.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AR/MA model experiments\n\nAR(p) and MA(q) model exploration in MATLAB using custom sample autocorrelation and sample partial autocorrelation implementations.\n\n## AR(2) example\n\nThe following picture shows ACF and PACF plots for an AR(2) process with the parameters\n\n```\ny(0) = -42\ny(t) = 0.7 y(t-1) + 0.2 y(t-2) + WN(my=0, sigma=1) \n```\n\nThe ACF plot shows dampened sinusoidal behavior, indicating an AR(p) process, while the PACF shows no significant value after lag p=2, indicating AR(2).\n\n![AR(2) model ACF and PACF](ar2_acf_pacf.jpg)\n\n## MA(1) example\n\nThe following picture shows ACF and PACF plots for an MA(1) process with the parameters\n\n```\ny(0) = epsilon(0) - 42\ny(t) = 0.8 epsilon(t-1) - 42 \n```\n\n![MA(1) model ACF and PACF](ma1_acf_pacf.jpg)\n\nThe PACF plot shows dampened sinusoidal behavior, indicating an MA(q) process, while the ACF shows no significant value after lag p=1, indicating MA(1).\n\n## 95% confidence intervals\n\nThe confidence intervals of the ACF and PACF plots are set to `± 1.96/√N`. Here `N` is the number of observations and `1.96` is the number of standard deviations 95% of the correlation values are expected lie within in under the assumption of the null hypothesis of no correlation. ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsunsided%2Farima","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsunsided%2Farima","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsunsided%2Farima/lists"}