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

The video tutorial covers the process of using the ARIMA model for time series forecasting. It begins with importing the ARIMA model from the statsmodels library, followed by selecting the model and understanding the significance of the order parameters (P, Q, D). The tutorial then demonstrates fitting the model to pollution data, generating a summary, and performing forecasting. It explores the impact of changing the order on model performance and introduces the concept of auto ARIMA for automatic order selection.

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