Simple Moving Average Forecasting Explained

The simple moving average (SMA) is a widely used forecasting method. It calculates the average of a specific number of past data points to predict the future value. For instance, if you use a 5-period simple moving average, you would average the values from the last five periods to get the forecast for the next period.

Forecast Sensitivity and Averaging Period

Forecast sensitivity refers to how quickly and strongly a forecast reacts to changes or fluctuations in the actual data. A highly sensitive forecast will adjust rapidly to new information, whereas a less sensitive forecast will be slower to respond to changes.

The length of average period (often denoted as \(N\)) in a simple moving average significantly influences this sensitivity:

• Shorter Averaging Period: When the averaging period \(N\) is short (e.g., N=3), the forecast is based on very recent data. This makes the forecast highly sensitive to recent changes and short-term fluctuations. It reacts quickly to emerging trends but might also be more susceptible to random noise in the data.
• Longer Averaging Period: When the averaging period \(N\) is long (e.g., N=10), the forecast incorporates data from a more extended past. By including a larger number of past data points, the forecast becomes smoother and less reactive to immediate variations. This means its sensitivity to sudden changes will decrease. The forecast becomes more stable but slower to reflect new conditions.

Understanding Lagging Trend in Forecasting

A lagging trend means that the forecast consistently trails behind the actual data's movement. Since the simple moving average relies solely on historical data, it inherently reflects past patterns and cannot anticipate future shifts or sudden changes.

• When the length of average period increases, the forecast includes data from even further back in time. This makes the forecast even slower to recognize and adjust to new upward or downward trends in the actual data.
• Consequently, a longer averaging period results in a more pronounced lagging trend. The forecast will eventually follow the actual trend, but always with a delay. The greater the averaging period, the greater the lag.

Conclusion on Forecast Behavior

Therefore, for the simple moving average forecasting method, an increase in the length of average period leads to two key outcomes:

• A decrease in forecast sensitivity: The forecast becomes smoother and less reactive to immediate data fluctuations because it averages over a longer period, dampening the impact of recent changes.
• A more significant lagging trend: The forecast will follow the actual data's trend with a greater delay, as it is based on a broader historical window and is slower to incorporate current shifts.

This explains why the forecast's sensitivity will decrease but with the lagging trend becoming more evident.