In the previous lesson, you learned a bit about Time Series Forecasting and loaded a dataset showing the fluctuations of electrical load over a time period.
[](https://youtu.be/IUSk-YDau10 "Introduction to ARIMA")
> A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
## [Pre-lecture quiz](link-to-quiz-app)
In the previous lesson, you learned a bit about Time Series Forecasting and loaded a dataset showing the fluctuations of electrical load over a time period. In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://wikipedia.org/wiki/Stationary_process).
In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://wikipedia.org/wiki/Stationary_process).
> 🎓 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'.
Jen Looper
3 years ago
committed by
GitHub