{"id":25865323,"url":"https://github.com/jcaperella29/sarima-model-in-python","last_synced_at":"2025-06-10T17:36:00.707Z","repository":{"id":231126790,"uuid":"780989363","full_name":"jcaperella29/SARIMA-model-in-python","owner":"jcaperella29","description":"This is python script for building a Autoregressive integrated moving average model . This example is predicting sales over several months.","archived":false,"fork":false,"pushed_at":"2024-04-03T21:39:28.000Z","size":15,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-02T01:37:32.163Z","etag":null,"topics":["data-science","machine-learning","python","sarima"],"latest_commit_sha":null,"homepage":"","language":"Python","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/jcaperella29.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":"2024-04-02T14:42:37.000Z","updated_at":"2025-01-08T17:01:15.000Z","dependencies_parsed_at":"2025-03-02T01:34:31.488Z","dependency_job_id":"3bfd52c5-b714-4dc2-aa29-01dbd16c2786","html_url":"https://github.com/jcaperella29/SARIMA-model-in-python","commit_stats":null,"previous_names":["jcaperella29/arima-model-in-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcaperella29%2FSARIMA-model-in-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcaperella29%2FSARIMA-model-in-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcaperella29%2FSARIMA-model-in-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcaperella29%2FSARIMA-model-in-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jcaperella29","download_url":"https://codeload.github.com/jcaperella29/SARIMA-model-in-python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcaperella29%2FSARIMA-model-in-python/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259118453,"owners_count":22807981,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["data-science","machine-learning","python","sarima"],"created_at":"2025-03-02T01:34:25.191Z","updated_at":"2025-06-10T17:36:00.677Z","avatar_url":"https://github.com/jcaperella29.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SARIMA-model-in-python\nThis is python script for building a Seasonal -Autoregressive integrated moving average model . This example is predicting sales over several months.\n\nFirst needed modules are imported , and scientific notation is removed.\nThen the data is read in and examined.\nNext, functions are built and used to add fields to the data that provide the year and month\nThen  we aggregate Sales Quantity for each month\nNext we use the melt function to convert the matrix of aggretated data into a single column\nThen the total sales quanity per month is visualized.\nThen the best parameters for the model are detrimined  after a related function is built.\nAfter that the model is tuned with the best parameters and the predicitons are made\nThen  the forecasts are then made into a dataframe along with the related month for each prediciton. \nThe dataframe is then exported as a CSV file.\nLastly, the model accuracy is measured and a plot displaying the predictions for the months is created\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcaperella29%2Fsarima-model-in-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcaperella29%2Fsarima-model-in-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcaperella29%2Fsarima-model-in-python/lists"}