Differentiate between the autoregressive and moving average models of time series.

Autoregressive (AR) and Moving Average (MA) models are fundamental components of time series analysis, a statistical method used to understand and predict data points collected over time. Both models are employed to capture and model the underlying patterns and dynamics within time series data, but they operate on different principles.

### Autoregressive (AR) Model:

The Autoregressive model, abbreviated as AR, assumes that the current value of a variable depends linearly on its past values and a random error term. In other words, it suggests that each observation in the time series is a linear combination of past observations and a stochastic component.

1. Formula: The AR model of order \( p \) is represented as:

\[ X_t = c + \phi_1 X_{t-1} + \phi_2 X_{t-2} + \dots + \phi_p X_{t-p} + \varepsilon_t \]

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