Answer: Both I and II are correct

**Explanation:** **Statement I:** This statement is correct. An ARMA (Autoregressive Moving Average) process indeed combines two components: 1. **AR (Autoregressive) component**: Uses lagged values of the time series itself (observed lagged time series) 2. **MA (Moving Average) component**: Uses lagged values of the random error terms (lagged unobservable random shocks) **Statement II:** This statement is also correct. ARMA processes typically exhibit gradually-decaying autocorrelations. The autocorrelation function (ACF) of an ARMA process decays gradually rather than cutting off abruptly, which is a characteristic feature that distinguishes ARMA processes from pure AR or MA processes. **Key Points:** - ARMA(p,q) models combine AR(p) and MA(q) components - They are more flexible than pure AR or MA models - The ACF pattern helps identify ARMA processes in time series analysis - Both statements accurately describe fundamental characteristics of ARMA processes

Author: Nikitesh Somanthe