FAQ 1: What is the difference between AR(p) and ARIMA models?

AR(p) models consider only the autoregressive component, while ARIMA models combine autoregressive, differencing, and moving average components. ARIMA models are more flexible and can handle both trended and stationary time series data.

FAQ 2: Can autoregressive models handle non-linear relationships?

Autoregressive models assume linearity, which may limit their ability to capture non-linear relationships. In such cases, more advanced models, like neural networks or support vector machines, may be more suitable.

FAQ 3: How do I choose the appropriate order (AR(p)) for my autoregressive model?

The appropriate order for your autoregressive model can be determined through various statistical techniques, such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC). These criteria help select the order that minimizes prediction errors.