@@ -24,7 +24,7 @@ In the last lesson you learned about ARIMA, which is a very successful statistic
The first few steps for data preparation are the same as that of the previous lesson on [ARIMA](https://github.com/microsoft/ML-For-Beginners/tree/main/7-TimeSeries/2-ARIMA).
Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.[^2]
Open the [_/working_](https://github.com/microsoft/ML-For-Beginners/tree/main/7-TimeSeries/3-SVR/working) folder in this lesson and find the _notebook.ipynb_ file.[^2]
1. Run the notebook and import the necessary libraries: [^2]
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@@ -383,4 +383,4 @@ This lesson was to introduce the application of SVR for Time Series Forecasting.
[^1]:The text, code and output in this section was contributed by [@AnirbanMukherjeeXD](https://github.com/AnirbanMukherjeeXD)
[^2]:The text, code and output in this section was taken from [ARIMA](https://github.com/microsoft/ML-For-Beginners/tree/main/7-TimeSeries/2-ARIMA)
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[^2]:The text, code and output in this section was taken from [ARIMA](https://github.com/microsoft/ML-For-Beginners/tree/main/7-TimeSeries/2-ARIMA)
