Merge pull request #537 from JoaquinAmatRodrigo/0.10.x

added new examples
JoaquinAmatRodrigo · Sep 7, 2023 · 754dbbb · 754dbbb
2 parents 08c62ee + ccc48e6
commit 754dbbb
Showing 1 changed file with 10 additions and 4 deletions.
diff --git a/README.md b/README.md
@@ -147,12 +147,16 @@ The **skforecast** library offers a variety of forecaster types, each tailored t
 
 + [**Skforecast: time series forecasting with Python and Scikit-learn**](https://www.cienciadedatos.net/documentos/py27-time-series-forecasting-python-scikitlearn.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1X1DJF4pZlklIt5srQnyTYoyFVLunr_OQ)
 
++ [**ARIMA and SARIMAX models**](https://www.cienciadedatos.net/documentos/py51-arima-sarimax-models-python.html)
+
++ [**Forecasting with gradient boosting: skforecast, XGBoost, LightGBM and CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Imy8ZM3DqPXg7UllRDH9gqWb_XSrqzzh)
+
++ [**Modelling time series trend with tree based models**](https://www.cienciadedatos.net/documentos/py49-modelling-time-series-trend-with-tree-based-models.html)
+
 + [**Forecasting electricity demand with Python**](https://www.cienciadedatos.net/documentos/py29-forecasting-electricity-power-demand-python.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1efCKQtuHOlw7MLojIwqi2zrU2NZbG-FP)
 
 + [**Forecasting web traffic with machine learning and Python**](https://www.cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1QhLkJAAEfvgYoVkQXy58-T_sloNFCV1o)
 
-+ [**Forecasting with gradient boosting: skforecast, XGBoost, LightGBM and CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Imy8ZM3DqPXg7UllRDH9gqWb_XSrqzzh)
-
 + [**Bitcoin price prediction with Python**](https://www.cienciadedatos.net/documentos/py41-forecasting-cryptocurrency-bitcoin-machine-learning-python.html)
 
 + [**Prediction intervals in forecasting models**](https://www.cienciadedatos.net/documentos/py42-forecasting-prediction-intervals-machine-learning.html)
@@ -166,16 +170,18 @@ The **skforecast** library offers a variety of forecaster types, each tailored t
 + [**Intermittent demand forecasting**](https://www.cienciadedatos.net/documentos/py48-intermittent-demand-forecasting.html)
 
 
+
+
 **Español**
 
 + [**Skforecast: forecasting series temporales con Python y Scikit-learn**](https://www.cienciadedatos.net/documentos/py27-forecasting-series-temporales-python-scikitlearn.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1mjmccrMA-XxOVXm-3wKSIQ9__oo9dJ5a)
 
++ [**Forecasting con gradient boosting: skforecast, XGBoost, LightGBM y CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-series-temporales-con-skforecast-xgboost-lightgbm-catboost.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1UAjX8vUKDoY0XJtq5WtHlJ4qwPvSgLrD)
+
 + [**Forecasting de la demanda eléctrica**](https://www.cienciadedatos.net/documentos/py29-forecasting-demanda-energia-electrica-python.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/15kQpANRBCLfNf77nmNcV6GjGPoYdOmmF)
 
 + [**Forecasting de las visitas a una página web**](https://www.cienciadedatos.net/documentos/py37-forecasting-visitas-web-machine-learning.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1uw2nyjA9XMcstfkpbWC4zCULN7Qp7MWV)
 
-+ [**Forecasting con gradient boosting: skforecast, XGBoost, LightGBM y CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-series-temporales-con-skforecast-xgboost-lightgbm-catboost.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1UAjX8vUKDoY0XJtq5WtHlJ4qwPvSgLrD)
-
 + [**Predicción del precio de Bitcoin con Python**](https://www.cienciadedatos.net/documentos/py41-forecasting-criptomoneda-bitcoin-machine-learning-python.html)
 
 + [**Workshop predicción de series temporales con machine learning Universidad de Deusto / Deustuko Unibertsitatea**](https://youtu.be/MlktVhReO0E)