From c3490a99a7f080e5903876b941c98a6bd8e28191 Mon Sep 17 00:00:00 2001 From: softchris Date: Fri, 25 Jun 2021 20:58:09 +0100 Subject: [PATCH] fixes --- 7-TimeSeries/2-ARIMA/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index fc76bcf83..d54a781be 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -14,7 +14,7 @@ In this lesson, you will discover a specific way to build models with [ARIMA: *A ## General concepts -To be able to work with ARIMA, there's some concepts you need to know about: +To be able to work with ARIMA, there are some concepts you need to know about: - 🎓 **Stationarity**. From a statistical context, stationarity refers to data whose distribution does not change when shifted in time. Non-stationary data, then, shows fluctuations due to trends that must be transformed to be analyzed. Seasonality, for example, can introduce fluctuations in data and can be eliminated by a process of 'seasonal-differencing'.