From fa27bbe7b7b0f6f7204c982a7c4773013a5d314c Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 4 Feb 2020 19:50:35 +0000 Subject: [PATCH] updated url --- examples/01_prepare_data/data_prepare_retail.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/01_prepare_data/data_prepare_retail.ipynb b/examples/01_prepare_data/data_prepare_retail.ipynb index b1b82a5a..dbc33a1a 100644 --- a/examples/01_prepare_data/data_prepare_retail.ipynb +++ b/examples/01_prepare_data/data_prepare_retail.ipynb @@ -17,7 +17,7 @@ "\n", "This notebook introduces how to split the Orange Juice dataset into training sets and test sets for training and evaluating different retail sales forecasting methods.\n", "\n", - "We use backtesting a method that tests a predictive model on historical data to evaluate the forecasting methods. Other than standard [K-fold cross validation](https://en.wikipedia.org/wiki/Cross-validation_\\(statistics\\)#k-fold_cross-validation) which randomly splits data into K folds, we split the data so that any of the time stamps in the training set is no later than any of the time stamps in the test set to ensure that no future information is used (expect certain information that we can know beforehand, e.g., price of the product in the next few weeks as we can set the price manually).\n", + "We use backtesting a method that tests a predictive model on historical data to evaluate the forecasting methods. Other than standard [K-fold cross validation](https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29) which randomly splits data into K folds, we split the data so that any of the time stamps in the training set is no later than any of the time stamps in the test set to ensure that no future information is used (expect certain information that we can know beforehand, e.g., price of the product in the next few weeks as we can set the price manually).\n", "\n" ] },