{"id":24864806,"url":"https://github.com/edjoukou/forecasting-co2-emissions","last_synced_at":"2025-03-26T18:43:24.406Z","repository":{"id":274764166,"uuid":"923985385","full_name":"EDJOUKOU/Forecasting-CO2-emissions","owner":"EDJOUKOU","description":"Forecasting carbon footprint using a statistical methods (SARIMA, Holt-Winters \u0026 Baseline naive)","archived":false,"fork":false,"pushed_at":"2025-01-29T08:07:33.000Z","size":582,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-29T09:22:24.796Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/EDJOUKOU.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":"2025-01-29T07:43:44.000Z","updated_at":"2025-01-29T08:07:37.000Z","dependencies_parsed_at":"2025-01-29T09:22:26.799Z","dependency_job_id":"3373623b-b3ff-4a10-a612-6d208eb411d7","html_url":"https://github.com/EDJOUKOU/Forecasting-CO2-emissions","commit_stats":null,"previous_names":["edjoukou/forecasting-co2-emissions"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EDJOUKOU%2FForecasting-CO2-emissions","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EDJOUKOU%2FForecasting-CO2-emissions/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EDJOUKOU%2FForecasting-CO2-emissions/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EDJOUKOU%2FForecasting-CO2-emissions/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EDJOUKOU","download_url":"https://codeload.github.com/EDJOUKOU/Forecasting-CO2-emissions/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245717586,"owners_count":20661140,"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":[],"created_at":"2025-01-31T23:56:06.405Z","updated_at":"2025-03-26T18:43:24.382Z","avatar_url":"https://github.com/EDJOUKOU.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Forecasting-CO2-emissions\n\n## Project Overview\nThis project aims to forecast carbon emissions using statistical methods, including SARIMA, Holt-Winters, and the Baseline Naïve Approach.\n\n## Data Source\nThe dataset used for this project was collected in January 2025 and sourced from Kaggle. It is available for download at the following link: [Carbon Dioxide Emissions Dataset](https://www.kaggle.com/datasets/ucsandiego/carbon-dioxide).\n\n## Tools\n- Jupyter Notebook IDE for data analysis\n\n## Data Cleaning and Preparation\nIn the initial phase, the following tasks were performed:\n1. Data loading and inspection\n2. Handling data types\n3. Addressing missing values\n4. Renaming the target variable\n\n## Exploratory Data Analysis\n1. Basic information and statistics about the dataset\n2. Visualization of the target variable (CO2 emissions)\n3. Splitting the data into training and testing sets\n\n## Data Analysis (Modeling)\n1. SARIMA Model\n2. Triple Exponential Smoothing Model\n3. Comparison of forecasts with actual values, using the last season of the training set as a reference\n\n## Results\nBoth SARIMA and Triple Exponential Smoothing models demonstrated similar performance, outperforming the Naïve Baseline model, which relied on the last season of the data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedjoukou%2Fforecasting-co2-emissions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fedjoukou%2Fforecasting-co2-emissions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedjoukou%2Fforecasting-co2-emissions/lists"}