Which of the following is not a time-series model?

• A. $ARIMA$
• B. $Moving Average$
• C. $Exponential Smoothing$
• D. $Simple Linear Regression$

Answer: (D)

Verified step by step guidance

1. Step 1: Understand what a time-series model is. Time-series models are statistical methods used to analyze data points collected or recorded at successive points in time, often equally spaced. These models capture patterns such as trends, seasonality, and autocorrelation in the data.
2. Step 2: Review the given options and identify which are time-series models. ARIMA (AutoRegressive Integrated Moving Average) is a popular time-series model that combines autoregression, differencing (integration), and moving average components to model time-dependent data.
3. Step 3: Moving Average in the context of time series refers to a model that uses past error terms to model the current value, which is a component of time-series analysis and is part of ARIMA models.
4. Step 4: Exponential Smoothing is a time-series forecasting method that applies weighted averages of past observations, with weights decaying exponentially over time, making it suitable for time-dependent data.
5. Step 5: Simple Linear Regression, however, models the relationship between two variables assuming independence of observations and does not inherently account for time dependence or autocorrelation, so it is not considered a time-series model.