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TimeSeries/2-ARIMA/README.md
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@ -11,7 +11,7 @@ In this lesson, you will discover a specific way to build models with [ARIMA: *A
> 🎓 Stationarity, from a statistical context, 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'. > 🎓 Stationarity, from a statistical context, 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'.
> 🎓 [Differencing](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing) data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series."[Paper by Shixiong et al](https://arxiv.org/abs/1904.07632) > 🎓 [Differencing](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing) data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series." [Paper by Shixiong et al](https://arxiv.org/abs/1904.07632)
Let's unpack the parts of ARIMA to better understand how it helps us model Time Series and help us make predictions against it. Let's unpack the parts of ARIMA to better understand how it helps us model Time Series and help us make predictions against it.
## AR - for AutoRegressive ## AR - for AutoRegressive

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