Singular Spectrum Analysis and ARIMA models implemented on Berkeley Earth Surface Time Series
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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.
Kaggle Notebook for visualizations and ARIMA
- statsmodel.api 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.
- Python scientific stack is used to simplify all implementations - NumPy, Pandas, SciPy, Seaborn, Matplotlib
- PNG Output of all plots are in the Output folder
Project Link: https://github.com/sn2606/Global-Temperature-Time-Series
- This Kaggle tutorial notebook
- Github Rebository - pssa
- Deng, Cheng, "Time Series Decomposition Using Singular Spectrum Analysis" (2014). Electronic Theses and Dissertations. Paper 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.
- ARIMA Model Python Example — Time Series Forecasting
- Time Series Data Visulaization with Python