Which of the following techniques is NOT considered a time-series forecasting method?

The correct answer identifies linear regression as not being a time-series forecasting method. Time-series forecasting methods specifically rely on historical data across time to predict future values, focusing on patterns such as trends and seasonality present in the dataset.

The naive approach, exponential smoothing, and simple moving average are all time-series techniques that utilize historical data to produce forecasts. For example, the naive approach uses the last observed value as the next forecast, assuming that past values will continue into the future. Exponential smoothing assigns exponentially decreasing weights to past observations, which helps in making more responsive forecasts. A simple moving average takes the average of a set number of past values, smoothing out fluctuations to highlight longer-term trends.

In contrast, linear regression involves modeling relationships among variables rather than relying strictly on time-ordered observations. It is often used in situations where the forecast depends on one or more predictor variables rather than solely on historical time sequence data. This distinction makes linear regression a different category of forecasting technique, primarily suited for situations where relationships among various factors are being analyzed.