Which forecasting method can help smooth out short-term fluctuations in data?

The moving averages method is particularly effective at smoothing out short-term fluctuations in data because it focuses on averaging a set number of past observations to generate a forecast. By taking the average of data points over a defined period, moving averages reduce the impact of random variations, allowing for a clearer trend to emerge. As new data points are added, the oldest observations are dropped, which continuously updates the forecast while still minimizing noise from short-term fluctuations.

For instance, in a business context, using a three-month moving average might help a company identify underlying trends in sales data, even when there are occasional spikes or drops in monthly sales figures. This makes moving averages a popular choice for analysts who are trying to assess longer-term trends while minimizing the influence of short-lived anomalies in their data.