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https://github.com/sn2606/global-temperature-time-series

Time series analysis is performed on the Berkeley Earth Surface Temperature dataset.
https://github.com/sn2606/global-temperature-time-series

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

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Time series analysis is performed on the Berkeley Earth Surface Temperature dataset.

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README 

        








  

  
Global Temperature Time Series Analysis


  

  


    Singular Spectrum Analysis and ARIMA models implemented on Berkeley Earth Surface Time Series

    


    Report Bugs

  



#

The 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.



[Link to the dataset](https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data)

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[Kaggle Notebook for visualizations and ARIMA](https://www.kaggle.com/swaranjananayak/global-temperatures-time-series-analysis)

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[Kaggle Notebook for SSA](https://www.kaggle.com/swaranjananayak/singular-spectrum-analysis-forecast)

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## Few points



* [statsmodel.api](https://www.statsmodels.org/stable/index.html) is used for ARIMA implementation

* SSA (decomposition, reconstruction, forecasting) is implemented from scratch as presented in [Golyandina, Nina & Zhigljavsky, Anatoly. (2013). Singular Spectrum Analysis for Time Series. 10.1007/978-3-642-34913-3.](https://www.springer.com/gp/book/9783662624357)

* Python scientific stack is used to simplify all implementations - NumPy, Pandas, SciPy, Seaborn, Matplotlib

* PNG Output of all plots are in the Output folder



## Decompositions and outputs



### Decomposition of time series for ARIMA

![decomp-arima]



### Forecast for ARIMA - mse = 0.09 (on same data for last 12 points i.e. year 2015)

![forecast-arima]



### Decomposition of time series for SSA

![decomp-ssa]



### Forecast for SSA - mse = 0.085 (on same data for last 12 points i.e. year 2015)

![forecast-ssa]



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## Contact



[@LinkedIn](https://www.linkedin.com/in/swaranjana-nayak/) - [email protected]



Project Link: [https://github.com/sn2606/Global-Temperature-Time-Series](https://github.com/sn2606/Global-Temperature-Time-Series)



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## Acknowledgements



* [This Kaggle tutorial notebook](https://www.kaggle.com/jdarcy/introducing-ssa-for-time-series-decomposition)

* [Github Rebository - pssa](https://github.com/aj-cloete/pssa)

* [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/)

* [Golyandina, Nina & Zhigljavsky, Anatoly. (2013). Singular Spectrum Analysis for Time Series. 10.1007/978-3-642-34913-3.](https://www.springer.com/gp/book/9783662624357)

* [ARIMA Model Python Example — Time Series Forecasting](https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7)

* [Time Series Data Visulaization with Python](https://machinelearningmastery.com/time-series-data-visualization-with-python/)



[contributors-shield]: https://img.shields.io/github/contributors/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge

[contributors-url]: https://github.com/sn2606/Global-Temperature-Time-Series/graphs/contributors

[forks-shield]: https://img.shields.io/github/forks/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge

[forks-url]: https://github.com/sn2606/Global-Temperature-Time-Series/network/members

[stars-shield]: https://img.shields.io/github/stars/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge

[stars-url]: https://github.com/sn2606/Global-Temperature-Time-Series/stargazers

[issues-shield]: https://img.shields.io/github/issues/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge

[issues-url]: https://github.com/sn2606/Global-Temperature-Time-Series/issues

[license-shield]: https://img.shields.io/github/license/sn2606/Global-Temperature-Time-Series.svg?style=for-the-badge

[license-url]: https://github.com/sn2606/Global-Temperature-Time-Series/blob/master/LICENSE.txt

[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555

[linkedin-url]: https://linkedin.com/in/sn2606

[decomp-arima]: Output/decomposition.png

[forecast-arima]: Output/forecast.png

[decomp-ssa]: Output/lat-components-grouped-sep.png

[forecast-ssa]: Output/forecast-ssa.png