FAQ 4: Are autoregressive models suitable for forecasting long-term trends in time series data?

Autoregressive models are primarily designed to capture short-term dependencies in the data. For forecasting long-term trends, it is often necessary to incorporate additional components, such as moving average or trend components.

FAQ 5: Can autoregressive models be used for outlier detection?

While autoregressive models can detect outliers to some extent, they may not be the most robust method for outlier detection. Other techniques, such as clustering, anomaly detection algorithms, or time series decomposition, can provide better insights into identifying outliers.

By following this guide, you have gained a thorough understanding of autoregressive models and their significance in time series analysis. Now, you can confidently apply these techniques to your own data and unlock valuable insights for various applications. Happy modelling!