In this one, you will get a perspective of what ARIMA is all about. And when you can and cannot use this model.
You’ll also understand the parameters of ARIMA model.
After you have a stationary time-series, you can either go for AR model or MA model. Or you can combine both into an ARMA and ARIMA.
AR means auto-regressive model. Learn how to build auto-regressive model and understand PACF in depth.
Use the data of restaurant visitor (data is part of project course), and split it into train and test data.
Learn how to implement ARIMA on this data set and compare the results.
Use the PACF and ACF plots to iterate between the parameters of ARIMA.
Check results of the model. And learn how to interpret and improve the forecast.
As a part of Restaurant Visitor Forecasting project course, learn all time series concepts from scratch (zero experience needed) and apply them across multiple approaches.
Check out the complete time series concepts + projects course