From 55eba2e393748b1ed97dfe19255012ee534b6e98 Mon Sep 17 00:00:00 2001 From: Anirban Mukherjee Date: Wed, 6 Oct 2021 11:55:41 +0530 Subject: [PATCH] Update README.md --- 7-TimeSeries/3-SVR/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md index d7d20422..0b5d5750 100644 --- a/7-TimeSeries/3-SVR/README.md +++ b/7-TimeSeries/3-SVR/README.md @@ -17,7 +17,7 @@ Before understanding the importance of SVR in time series prediction, here are s ### Why SVR? -In the last lesson you learned about ARIMA, which is a very successful statistical linear method to forecast time series data. However, in many cases, time series data have *non-linearity*, which cannot be mapped by linear models. The ability of SVM to consider nonlinearity in the data for regression tasks makes SVR successful in time series forecasting. +In the last lesson you learned about ARIMA, which is a very successful statistical linear method to forecast time series data. However, in many cases, time series data have *non-linearity*, which cannot be mapped by linear models. In such cases, the ability of SVM to consider non-linearity in the data for regression tasks makes SVR successful in time series forecasting. ## Exercise - build an SVR model @@ -103,14 +103,14 @@ Now, you need to prepare the data for training by performing filtering and scali Test data shape: (48, 1) ``` -2. Scale the data to be in the range (0, 1). +2. Scale the training data to be in the range (0, 1). ```python scaler = MinMaxScaler() train['load'] = scaler.fit_transform(train) ``` -4. Now, you scale the test data: +4. Now, you scale the testing data: ```python test['load'] = scaler.transform(test)