A Practical Approach to Timeseries Forecasting Using Python - Auto ARIMA in Python

The video tutorial explains how to use Auto ARIMA from PMD ARIMA to build and fit a model for time series data. It covers the installation of necessary packages, setting parameters like trace and error action, and fitting the model to data. The tutorial also discusses adjusting model settings, making predictions, and evaluating results through plotting. The best model order is identified as 002, and the process is demonstrated with code examples.

What changes are made to the model fitting process in the provided text?

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How is the prediction made using the fitted model?

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What is the final output of the ARIMA calculation as described in the text?

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