From 0f356fc8b3e0badf7960f8ac6184e721457bb487 Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Sun, 20 Jun 2021 11:20:30 -0400 Subject: [PATCH] ARIMA format --- 7-TimeSeries/2-ARIMA/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index ec6d90f14..8ec821be1 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -109,7 +109,7 @@ Therefore, using a relatively small window of time for training the data should Now, you need to prepare the data for training by performing two tasks: - 1. Filter the original dataset to include only the aforementioned time periods per set and only including the needed column 'load' plus the date: + - Filter the original dataset to include only the aforementioned time periods per set and only including the needed column 'load' plus the date: ```python train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']] @@ -125,7 +125,7 @@ print('Test data shape: ', test.shape) Training data shape: (1416, 1) Test data shape: (48, 1) ``` - 2. Scale the data to be in the range (0, 1). + - Scale the data to be in the range (0, 1). ```python scaler = MinMaxScaler()