Merge branch 'main' into parallelization-document-extras

blue-yonder · Oct 16, 2023 · b952405 · b952405
2 parents ae93811 + 3ec96dc
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diff --git a/docs/text/introduction.rst b/docs/text/introduction.rst
@@ -9,7 +9,7 @@ These data have in common that they are ordered by an independent variable.
 The most common independent variable is time (time series).
 Other examples for sequential data are reflectance and absorption spectra,
 which have wavelength as their ordering dimension.
-In order keeps things simple, we are simplify referring to all different types of sequential data as time-series.
+In order to keep things simple, we are simply referring to all different types of sequential data as time-series.
 
 .. image:: ../images/introduction_ts_exa.png
  :scale: 70 %

diff --git a/docs/text/quick_start.rst b/docs/text/quick_start.rst
@@ -143,7 +143,7 @@ You can now use the features in the DataFrame `features_filtered` (which is equa
 `features_filtered_direct`) in conjunction with `y` to train your classification model.
 You can find an example in the Jupyter notebook
 `01 Feature Extraction and Selection.ipynb <https://github.com/blue-yonder/tsfresh/blob/main/notebooks/01%20Feature%20Extraction%20and%20Selection.ipynb>`_
-were we train a RandomForestClassifier using the extracted features.
+where we train a RandomForestClassifier using the extracted features.
 
 References