What type of forecasting is represented by moving averages and exponential smoothing?

Moving averages and exponential smoothing are both techniques used to analyze and forecast future data points based on historical values. These methods fall under the category of time series forecasting because they rely exclusively on past observations within a dataset to predict future trends.

Time series forecasting is grounded in the idea that data points collected over time can demonstrate patterns that repeat, such as trends, cycles, or seasonal variations. By applying moving averages, which smooth out fluctuations in the data by averaging over a specific number of periods, these techniques can effectively capture the underlying trend without being overly influenced by random variations. Similarly, exponential smoothing gives more weight to recent observations while still considering older data, allowing for a responsive yet stable forecasting model.

On the other hand, causal models rely on the relationships between variables to predict outcomes, which is not the case with moving averages and exponential smoothing. Subjective models and qualitative methods, in contrast, are based on personal judgment and insights, rather than purely on numerical data trends. Thus, the identification of moving averages and exponential smoothing as part of time series forecasting underscores their reliance on historical data to inform predictions.