Now, it's time to implement SVR. To read more about this implementation, you can refer to [this documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html). For our implementation, we follow these steps:
1. Define the model by calling `SVR()` and passing in the model hyperparameters: kernel, gamma, c and epsilon
2. Prepare the model for the training data by calling the `fit()` function.
2. Prepare the model for the training data by calling the `fit()` function
3. Make predictions calling the `predict()` function
Now we create an SVR model. Here we use the [RBF kernel](https://scikit-learn.org/stable/modules/svm.html#parameters-of-the-rbf-kernel), and set the hyperparameters gamma, C and epsilon as 0.5, 10 and 0.05 respectively.
@ -361,6 +361,10 @@ MAPE: 2.0572089029888656 %
- Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions).
- Try using different values for `timesteps` for the model to look back to make prediction.
## Review & Self Study
This lesson was to introduce the application of SVR for Time Series Forecasting. To read more about SVR, you can refer to [this blog](https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/). This [documentation on scikit-learn](https://scikit-learn.org/stable/modules/svm.html) provides a more comprehensive explanation about SVMs in general, [SVRs](https://scikit-learn.org/stable/modules/svm.html#regression) and also other implementation details such as the different [kernel functions](https://scikit-learn.org/stable/modules/svm.html#kernel-functions) that can be used, and their parameters.
Anirban Mukherjee
4 years ago