fix link

Author: EricWrightAtWork

articles/machine-learning/concept-automl-forecasting-methods.md

@@ -46,7 +46,7 @@ The following table lists the forecasting models implemented in AutoML and what
 
 Time Series Models | Regression Models
 -------------------| -----------------
-[Naive, Seasonal Naive, Average, Seasonal Average](https://otexts.com/fpp3/simple-methods.html), [ARIMA(X)](https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html), [Exponential Smoothing](https://www.statsmodels.org/dev/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html) | [Linear SGD](https://scikit-learn.org/stable/modules/linear_model.html#stochastic-gradient-descent-sgd), [LARS LASSO](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso), [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net), [Prophet](https://facebook.github.io/prophet/), [K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression), [Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression), [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests), [Extremely Randomized Trees](https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees), [Gradient Boosted Trees](https://scikit-learn.org/stable/modules/ensemble.html#regression), [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html), [XGBoost](https://xgboost.readthedocs.io/en/latest/parameter.html), [TCNForecaster](./concept-automl-forecasting-deep-learning.md#tcnforecaster)
+[Naive, Seasonal Naive, Average, Seasonal Average](https://otexts.com/fpp3/simple-methods.html), [ARIMA(X)](https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html), [Exponential Smoothing](https://www.statsmodels.org/dev/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html) | [Linear SGD](https://scikit-learn.org/stable/modules/linear_model.html#stochastic-gradient-descent-sgd), [LARS LASSO](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso), [Elastic Net](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net), [Prophet](https://facebook.github.io/prophet/), [K Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression), [Decision Tree](https://scikit-learn.org/stable/modules/tree.html#regression), [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests), [Extremely Randomized Trees](https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees), [Gradient Boosted Trees](https://scikit-learn.org/stable/modules/ensemble.html#regression), [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html), [XGBoost](https://xgboost.readthedocs.io/en/latest/parameter.html), [TCNForecaster](./concept-automl-forecasting-deep-learning.md#introduction-to-tcnforecaster)
 
 The models in each category are listed roughly in order of the complexity of patterns they're able to incorporate, also known as the **model capacity**. A Naive model, which simply forecasts the last observed value, has low capacity while the Temporal Convolutional Network (TCNForecaster), a deep neural network with potentially millions of tunable parameters, has high capacity.