Skip to content

Moving Average

  • Smooths short-term fluctuations in a data series to reveal the underlying trend.
  • Computed by averaging a fixed number of the most recent data points and updating as new data arrives.
  • Used for simple forecasting; longer averaging windows generally improve forecast stability.

Moving average is a technique used in technical analysis to smooth out short-term fluctuations in a data series and to highlight the underlying trend. It is calculated by taking the average of a certain number of past data points, typically over a specified period of time.

A moving average takes the mean of the most recent N observations (where N is the chosen period) to produce a smoothed value. As a new data point becomes available, the oldest point in the window is dropped and the newest point is included, producing an updated average. This filtering reduces the influence of short-term volatility and emphasizes longer-term movement in the series.

Closing prices for the last 10 days:

DayClosing price
Day 1$10
Day 2$11
Day 3$12
Day 4$13
Day 5$14
Day 6$13
Day 7$12
Day 8$11
Day 9$10
Day 10$9

The 10-day moving average is:

10+11+12+13+14+13+12+11+10+910=$11.50\frac{10 + 11 + 12 + 13 + 14 + 13 + 12 + 11 + 10 + 9}{10} = \$11.50

If a new closing price of $8 arrives (Day 11), the moving average is recalculated by dropping Day 1 and adding Day 11:

11+12+13+14+13+12+11+10+9+810=$11.00\frac{11 + 12 + 13 + 14 + 13 + 12 + 11 + 10 + 9 + 8}{10} = \$11.00

Sales forecasting — 3-month moving average

Section titled “Sales forecasting — 3-month moving average”

Monthly sales for 6 months:

MonthSales
Month 1$10,000
Month 2$12,000
Month 3$11,000
Month 4$13,000
Month 5$12,000
Month 6$11,000

To forecast the next month’s sales using a 3-month moving average (last 3 months: Month 2–4):

12,000+11,000+13,0003=$11,667\frac{12,000 + 11,000 + 13,000}{3} = \$11,667

The source notes that using only 3 data points may yield a less accurate forecast; using a longer moving average (for example, a 6-month moving average) would take into account all 6 months of data.

  • Smoothing short-term fluctuations to reveal the underlying trend (technical analysis).
  • Simple forecasting of future values using recent averages.
  • Forecasts based on very short moving averages (e.g., 3 points) may not be very accurate.
  • Increasing the length of the moving-average window (e.g., to 6 months) can improve forecast stability by incorporating more data.
  • Technical analysis
  • Forecasting