ARIMA-ML-Model-Time-Series-Forecast_dataset-Air-Passangers

Time Series Forecast: A basic introduction using Python (ML + DS)

Time series data is an important source for information and strategy used in various businesses. ARIMA is a form of regression analysis that indicates the strength of a dependent variable relative to other changing variables. The final objective of the model is to predict future time series movement by examining the differences between values in the series instead of through actual values.

Modeling procedure

When fitting an ARIMA model to a set of (non-seasonal) time series data, the following procedure provides a useful general approach.

1. Plot the data and identify any unusual observations.
2. If necessary, transform the data o stabilize the variance.
3. If the data are non-stationary, take first differences of the data until the data are stationary.
4. Examine the ACF/PACF: Is an ARIMA(p,d,0p,d,0) or ARIMA(0,d,q0,d,q) model appropriate?
5. Try your chosen model(s), and use the AICc to search for a better model.
6. Check the residuals from your chosen model by plotting the ACF of the residuals, and doing a portmanteau test of the residuals. If they do not look like white noise, try a modified model.
7. Once the residuals look like white noise, calculate forecasts.