• Autoregressive models are relatively easy to understand and implement.
• They provide interpretable coefficients, offering insights into the time series dynamics.
• These models can capture both short-term and long-term dependencies in the data.

Limitations

• Autoregressive models assume linearity and stationarity, which may not always hold in real-world scenarios.
• They can be sensitive to outliers and noise in the data, impacting the model’s accuracy.
• The performance of autoregressive models can deteriorate if the underlying data-generating process changes over time.

Implementing Autoregressive Models in Python

Installing Required Libraries

To implement autoregressive models in Python, we will need libraries like numpy, pandas, and statsmodels. Install them using the appropriate package manager or command.