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Timeseries: Exploratory data analysis with PyCaret
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Co-authored-by: ckurze <[email protected]>
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2 people authored and amotl committed Feb 27, 2024
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12 changes: 12 additions & 0 deletions topic/timeseries/README.md
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Expand Up @@ -28,5 +28,17 @@ repository, e.g. about machine learning, to see predictions and AutoML in action
window functions, interpolation of missing data, common table expressions,
moving averages, JOINs and the handling of JSON data.

- `exploratory_data_analysis.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](exploratory_data_analysis.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/timeseries/exploratory_data_analysis.ipynb)

This notebook explores how to access timeseries data from CrateDB via SQL,
and do the exploratory data analysis (EDA) with PyCaret.

It also shows how you can generate various plots and charts for EDA, helping you understand data distributions, relationships between variables, and identify patterns.

- `time-series-decomposition.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](time-series-decomposition.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/timeseries/time-series-decomposition.ipynb)

This notebook illustrates how to extract data from CrateDB and how to use PyCaret for time-series decomposition.

Furthermore, it shows how to preprocess data and plot time series decomposition by breaking it down into its basic components: trend, seasonality, and residual (or irregular) fluctuations.

[CrateDB]: https://github.com/crate/crate
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