Introduction to ARIMA Models for Newbies
(Si apre in una nuova finestra)ARIMA (short for AutoRegressive Integrated Moving Average) is a technique for time series analysis and forecasting. It is one of the most widely used approaches for time series forecasts. There are seasonal and non-seasonal ARIMA models, depending on the type of time series data.
ARIMA models are often used in the financial industry. They are fundamental to understanding time series analysis. In addition, ARIMA models can be very complex, which means that they are not always easy to understand.
In this tutorial, we would like to give you an easy-to-understand and visual introduction to ARIMA models.
We’ll discuss the following topics:
Introduction to ARIMA
Components of an ARIMA model
Stationary vs. Non-Stationary
Differentiation
Autocorrelation Plots
Practical example: ARIMA with statsmodels
Technical requirements
Use Case and Dataset
Time series visualization
Stationarity check
Autocorrelation Plots
Create ARIMA model
Forecasting with ARIMA
Conclusion
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