{"id":25654201,"url":"https://github.com/funinkina/covid19_data-time-series-analysis","last_synced_at":"2026-04-18T11:04:38.265Z","repository":{"id":240283539,"uuid":"802206832","full_name":"funinkina/Covid19_Data-Time-Series-Analysis","owner":"funinkina","description":"Time Series Analysis of Covid-19 Dataset","archived":false,"fork":false,"pushed_at":"2024-05-17T18:35:58.000Z","size":1082,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-23T20:18:32.114Z","etag":null,"topics":["arima-forecasting","arima-model","jupyer-notebook","machine-learning","prophet-forecasting","prophet-model","python","research-paper","research-project","sarima-model","time-series-analysis"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/funinkina.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":"2024-05-17T18:20:36.000Z","updated_at":"2024-05-21T15:00:41.000Z","dependencies_parsed_at":"2024-05-17T19:51:25.161Z","dependency_job_id":null,"html_url":"https://github.com/funinkina/Covid19_Data-Time-Series-Analysis","commit_stats":null,"previous_names":["funinkina/covid19_data-time-series-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/funinkina/Covid19_Data-Time-Series-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/funinkina%2FCovid19_Data-Time-Series-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/funinkina%2FCovid19_Data-Time-Series-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/funinkina%2FCovid19_Data-Time-Series-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/funinkina%2FCovid19_Data-Time-Series-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/funinkina","download_url":"https://codeload.github.com/funinkina/Covid19_Data-Time-Series-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/funinkina%2FCovid19_Data-Time-Series-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31966218,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T00:39:45.007Z","status":"online","status_checked_at":"2026-04-18T02:00:07.018Z","response_time":103,"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-forecasting","arima-model","jupyer-notebook","machine-learning","prophet-forecasting","prophet-model","python","research-paper","research-project","sarima-model","time-series-analysis"],"created_at":"2025-02-23T20:18:33.112Z","updated_at":"2026-04-18T11:04:38.215Z","avatar_url":"https://github.com/funinkina.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Time series analysis of Covid 19 date wise dataset  \n\n[Here is the dataset](https://www.kaggle.com/datasets/sudalairajkumar/covid19-in-india?select=covid_19_india.csv)  \n\n## Models Used:\n- Arima\n- Sarima\n- Prophet\n\nFirst we made the data stationary and then used SARIMA, ARIMA and Prophet models to determine `Cured`, `Deaths`, `Confirmed` cases for the next 30 days\nAnd used **matplotlib** to visualisation of the data  \nAlso calculated Mean, SE Mean, Standard Deviation, Minimum, Maximum, Skewness and Kurtosis\n\n\n\nThis is an implementation of this research paper\n[Wang Y, Yan Z, Wang D, Yang M, Li Z, Gong X, Wu D, Zhai L, Zhang W, Wang Y. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models. BMC Infect Dis. 2022 May 25;22(1):495. doi: 10.1186/s12879-022-07472-6. PMID: 35614387; PMCID: PMC9131989.](https://pubmed.ncbi.nlm.nih.gov/35614387/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffuninkina%2Fcovid19_data-time-series-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffuninkina%2Fcovid19_data-time-series-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffuninkina%2Fcovid19_data-time-series-analysis/lists"}