Which of the following is an example of moving average forecasting?

A moving average forecasting method involves calculating the average of a rolling window of recent data points to predict future values. This technique is simple and effective for forecasting when the data is relatively stable or when there is little trend or seasonality. The moving average smooths out short-term fluctuations in the data and helps identify underlying trends. Different types of moving averages can be used, such as simple moving average (SMA), weighted moving average (WMA), or exponential moving average (EMA). It is widely used in financial and stock market forecasting. The other options are incorrect because: • Option 1 (ARIMA) involves a more complex statistical approach that models time series data with autoregressive and moving average components, not a simple moving average. • Option 3 (Exponential smoothing) is a different forecasting method that assigns exponentially decreasing weights to past observations. • Option 4 (Decomposition) refers to breaking down a time series into trend, seasonal, and residual components, which is distinct from moving average forecasting. • Option 5 (Regression analysis) involves modeling the relationship between variables, not averaging a window of data points.