{"id":21747306,"url":"https://github.com/sn2606/global-temperature-time-series","last_synced_at":"2026-05-21T14:14:57.586Z","repository":{"id":203694782,"uuid":"350010992","full_name":"sn2606/Global-Temperature-Time-Series","owner":"sn2606","description":"Time series analysis is performed on the Berkeley Earth Surface Temperature dataset.","archived":false,"fork":false,"pushed_at":"2021-05-23T09:14:07.000Z","size":11565,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-25T22:53:56.032Z","etag":null,"topics":["arima","arima-forecasting","arima-model","climate-change","data-analysis","data-visualization","forecasting-model","global-temperature","series-analysis","singular-spectrum-analysis","time-series","time-series-analysis","time-series-forecasting"],"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/sn2606.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}},"created_at":"2021-03-21T13:44:42.000Z","updated_at":"2024-07-12T06:56:43.000Z","dependencies_parsed_at":null,"dependency_job_id":"c4e9242a-abd4-47ff-a536-70b04f89d703","html_url":"https://github.com/sn2606/Global-Temperature-Time-Series","commit_stats":null,"previous_names":["sn2606/global-temperature-time-series"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sn2606%2FGlobal-Temperature-Time-Series","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sn2606%2FGlobal-Temperature-Time-Series/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sn2606%2FGlobal-Temperature-Time-Series/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sn2606%2FGlobal-Temperature-Time-Series/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sn2606","download_url":"https://codeload.github.com/sn2606/Global-Temperature-Time-Series/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244722742,"owners_count":20499154,"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":["arima","arima-forecasting","arima-model","climate-change","data-analysis","data-visualization","forecasting-model","global-temperature","series-analysis","singular-spectrum-analysis","time-series","time-series-analysis","time-series-forecasting"],"created_at":"2024-11-26T08:08:36.326Z","updated_at":"2026-05-21T14:14:57.536Z","avatar_url":"https://github.com/sn2606.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!--\n*** Thanks for checking out the Best-README-Template. If you have a suggestion\n*** that would make this better, please fork the Global-Temperature-Time-Series and create a pull request\n*** or simply open an issue with the tag \"enhancement\".\n*** Thanks again! Now go create something AMAZING! :D\n***\n*** https://github.com/sn2606/Global-Temperature-Time-Series\n***\n*** To avoid retyping too much info. Do a search and replace for the following:\n*** sn2606, Global-Temperature-Time-Series, https://www.linkedin.com/in/swaranjana-nayak/, swaranjananayak@gmail.com, Global Temperature Time Series Analysis, Singular Spectrum Analysis and ARIMA models implemented on Berkeley Earth Surface Time Series\n--\u003e\n\n\n\n\u003c!-- PROJECT SHIELDS --\u003e\n\u003c!--\n*** I'm using markdown \"reference style\" links for readability.\n*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).\n*** See the bottom of this document for the declaration of the reference variables\n*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.\n*** https://www.markdownguide.org/basic-syntax/#reference-style-links\n--\u003e\n\n\n\n\u003c!-- PROJECT LOGO --\u003e\n\u003cbr /\u003e\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/sn2606/Global-Temperature-Time-Series\"\u003e\n  \u003ch1 align=\"center\"\u003eGlobal Temperature Time Series Analysis\u003c/h1\u003e\n  \u003c/a\u003e\n  \u003cp\u003e\n    Singular Spectrum Analysis and ARIMA models implemented on Berkeley Earth Surface Time Series\n    \u003cbr /\u003e\n    \u003ca href=\"https://github.com/sn2606/Global-Temperature-Time-Series/issues\"\u003eReport Bugs\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/p\u003e\n\n#\nThe Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country).  Time series analysis is performed on this dataset.\n\n[Link to the dataset](https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data)\n#\n[Kaggle Notebook for visualizations and ARIMA](https://www.kaggle.com/swaranjananayak/global-temperatures-time-series-analysis)\n#\n[Kaggle Notebook for SSA](https://www.kaggle.com/swaranjananayak/singular-spectrum-analysis-forecast)\n#\n\n## Few points\n\n* [statsmodel.api](https://www.statsmodels.org/stable/index.html) is used for ARIMA implementation\n* SSA (decomposition, reconstruction, forecasting) is implemented from scratch as presented in [Golyandina, Nina \u0026 Zhigljavsky, Anatoly. (2013). Singular Spectrum Analysis for Time Series. 10.1007/978-3-642-34913-3.](https://www.springer.com/gp/book/9783662624357)\n* Python scientific stack is used to simplify all implementations - NumPy, Pandas, SciPy, Seaborn, Matplotlib\n* PNG Output of all plots are in the Output folder\n\n## Decompositions and outputs\n\n### Decomposition of time series for ARIMA\n![decomp-arima]\n\n### Forecast for ARIMA - mse = 0.09 (on same data for last 12 points i.e. year 2015)\n![forecast-arima]\n\n### Decomposition of time series for SSA\n![decomp-ssa]\n\n### Forecast for SSA - mse = 0.085 (on same data for last 12 points i.e. year 2015)\n![forecast-ssa]\n\n#\n\u003c!-- CONTACT --\u003e\n## Contact\n\n[@LinkedIn](https://www.linkedin.com/in/swaranjana-nayak/) - swaranjananayak@gmail.com\n\nProject Link: [https://github.com/sn2606/Global-Temperature-Time-Series](https://github.com/sn2606/Global-Temperature-Time-Series)\n\n#\n\u003c!-- ACKNOWLEDGEMENTS --\u003e\n## Acknowledgements\n\n* [This Kaggle tutorial notebook](https://www.kaggle.com/jdarcy/introducing-ssa-for-time-series-decomposition)\n* [Github Rebository - pssa](https://github.com/aj-cloete/pssa)\n* [Deng, Cheng, \"Time Series Decomposition Using Singular Spectrum Analysis\" (2014). Electronic Theses and Dissertations. Paper 2352. https://dc.etsu.edu/etd/2352](https://dc.etsu.edu/etd/2352/)\n* [Golyandina, Nina \u0026 Zhigljavsky, Anatoly. (2013). Singular Spectrum Analysis for Time Series. 10.1007/978-3-642-34913-3.](https://www.springer.com/gp/book/9783662624357)\n* [ARIMA Model Python Example — Time Series Forecasting](https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7)\n* [Time Series Data Visulaization with Python](https://machinelearningmastery.com/time-series-data-visualization-with-python/)\n\n\n\n\u003c!-- MARKDOWN LINKS \u0026 IMAGES --\u003e\n\u003c!-- https://www.markdownguide.org/basic-syntax/#reference-style-links --\u003e\n[contributors-shield]: https://img.shields.io/github/contributors/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge\n[contributors-url]: https://github.com/sn2606/Global-Temperature-Time-Series/graphs/contributors\n[forks-shield]: https://img.shields.io/github/forks/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge\n[forks-url]: https://github.com/sn2606/Global-Temperature-Time-Series/network/members\n[stars-shield]: https://img.shields.io/github/stars/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge\n[stars-url]: https://github.com/sn2606/Global-Temperature-Time-Series/stargazers\n[issues-shield]: https://img.shields.io/github/issues/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge\n[issues-url]: https://github.com/sn2606/Global-Temperature-Time-Series/issues\n[license-shield]: https://img.shields.io/github/license/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge\n[license-url]: https://github.com/sn2606/Global-Temperature-Time-Series/blob/master/LICENSE.txt\n[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge\u0026logo=linkedin\u0026colorB=555\n[linkedin-url]: https://linkedin.com/in/sn2606\n[decomp-arima]: Output/decomposition.png\n[forecast-arima]: Output/forecast.png\n[decomp-ssa]: Output/lat-components-grouped-sep.png\n[forecast-ssa]: Output/forecast-ssa.png\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsn2606%2Fglobal-temperature-time-series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsn2606%2Fglobal-temperature-time-series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsn2606%2Fglobal-temperature-time-series/lists"}