Moving average is a method used in trend analysis to smooth out short-term fluctuations in data and reveal the underlying long-term trend.
It works by calculating the average of a fixed number of consecutive data points and then moving that calculation forward through the dataset.
A moving average is the average of values over a specific time period that continuously updates as new data comes in, helping to show the overall direction of the trend.
Reduces short-term noise or fluctuations.
Highlights long-term patterns or direction.
Makes trends easier to understand and visualize.
Suppose monthly sales are:
| Month | Sales |
|---|---|
| Jan | 100 |
| Feb | 120 |
| Mar | 140 |
| Apr | 160 |
| May | 180 |
Jan–Mar → (100 + 120 + 140) ÷ 3 = 120
Feb–Apr → (120 + 140 + 160) ÷ 3 = 140
Mar–May → (140 + 160 + 180) ÷ 3 = 160
Moving average series: 120, 140, 160
This shows a clear upward trend.
Each value has equal weight.
Most basic and commonly used.
Recent values get more weight.
Gives exponentially higher weight to recent data.
Reacts faster to trend changes.
Easy to calculate and understand.
Smooths irregular data.
Useful for identifying trends.
Based on past data, so it lags behind actual changes.
Choice of period can affect results.