Update to README

topic/timeseries/README.md

@@ -31,8 +31,14 @@ repository, e.g. about machine learning, to see predictions and AutoML in action
 - `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 with PyCaret.
+  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

topic/timeseries/time-series-decomposition.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "Integrating PyCaret with CrateDB presents a compelling opportunity for handling large-scale data analytics and ML projects. CrateDB is a distributed SQL database that excels in handling massive amounts of structured and unstructured data in real-time. This integration allows users to leverage CrateDB's efficient data storage and fast query capabilities to manage large datasets, while PyCaret's ML algorithms can be applied directly to this data for predictive analytics, anomaly detection, and other advanced analytics tasks.\n",
     "\n",
-    "By following this notebook you will learn how to extract data from CrateDB for analysis in PyCaret, how to further prerocess it and how to use  PyCaret to plot time series decomposition by breaking it down into its basic components: **trend, seasonality, and residual (or irregular) fluctuations**."
+    "By following this notebook you will learn how to extract data from CrateDB for analysis in PyCaret, how to further preprocess it and how to use  PyCaret to plot time series decomposition by breaking it down into its basic components: **trend, seasonality, and residual (or irregular) fluctuations**."
    ]
   },
   {