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docs update - fixed broken links #2

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docs update - fixed broken links #2

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f370983
fix anchor link
neuralmaticv Aug 20, 2022
2463531
fixed broken links
neuralmaticv Aug 20, 2022
f7eb0d8
fixed broken links
neuralmaticv Aug 20, 2022
eded85a
Merge branch 'main' into main
neuralmaticv Jan 7, 2024
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -74,9 +74,9 @@ PyCaret and its Machine Learning capabilities are seamlessly integrated with env

### Time Series

PyCaret **new time series module** is now available in the `3.0-rc`. Staying true to the simplicity of PyCaret, it is consistent with the existing API and fully loaded with functionalities. Statistical testing, model training and selection (30+ algorithms), model analysis, automated hyperparameter tuning, experiment logging, deployment on cloud, and more. All of this with only a few lines of code. If you would like to give it a try, check out our official [quick start](https://nbviewer.org/github/pycaret/pycaret/blob/time\_series\_beta/time\_series\_101.ipynb) notebook.
PyCaret **new time series module** is now available in the `3.0-rc`. Staying true to the simplicity of PyCaret, it is consistent with the existing API and fully loaded with functionalities. Statistical testing, model training and selection (30+ algorithms), model analysis, automated hyperparameter tuning, experiment logging, deployment on cloud, and more. All of this with only a few lines of code. If you would like to give it a try, check out our official [quick start](https://nbviewer.org/github/pycaret/pycaret/blob/master/examples/time_series/forecasting/time_series_101.ipynb) notebook.
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I Am wondering if we should change the link to the folder now that we have multiple files there:

https://nbviewer.org/github/pycaret/pycaret/tree/master/examples/time_series/forecasting/

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That would be a better option, but in that case, I think that notebook names should be more informative.

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How about this in that case? It has more information about the notebooks.

https://pycaret.gitbook.io/docs/get-started/tutorials

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Yes, I think that would be fine.


* 📚 [Time Series Docs](https://pycaret.readthedocs.io/en/time\_series/api/time\_series.html)
* 📚 [Time Series Docs](https://pycaret.readthedocs.io/en/latest/api/time_series.html)
* ❓ [Time Series FAQs](https://github.com/pycaret/pycaret/discussions/categories/faqs?discussions\_q=category%3AFAQs+label%3Atime\_series)
* 🚀 [Features and Roadmap](https://github.com/pycaret/pycaret/issues/1648)

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6 changes: 3 additions & 3 deletions get-started/modules.md
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Expand Up @@ -68,12 +68,12 @@ Association rule learning is a rule-based machine learning method for discoverin
{% endtab %}

{% tab title="Time Series" %}
### [Time Series (beta)](quickstart.md#time-series-beta)
### [Time Series (beta)](quickstart.md#time-series)

Time series forecasting is the process of analyzing time series data using statistics and modeling to make predictions and inform strategic decision-making

* ****[**Quickstart**](quickstart.md#time-series-beta)****
* ****[**API Docs**](https://pycaret.readthedocs.io/en/time\_series/api/time\_series.html)****
* ****[**Quickstart**](quickstart.md#time-series)****
* ****[**API Docs**](https://pycaret.readthedocs.io/en/latest/api/time_series.html)****
* ****[**Tutorial**](tutorials.md)
{% endtab %}

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2 changes: 1 addition & 1 deletion get-started/quickstart.md
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Expand Up @@ -18,7 +18,7 @@ Select your use case:
* [Anomaly Detection](quickstart.md#anomaly-detection)
* [Natural Language Processing](quickstart.md#natural-language-processing)
* [Association Rules Mining](quickstart.md#association-rules-mining)
* [Time Series (beta)](quickstart.md#time-series-beta)
* [Time Series (beta)](quickstart.md#time-series)

## Classification

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