Simple moving average model:
Formula:
Y _ t = (1/k) * (y _ {t-1}+y _ {t-2}+...+y _ {t-k}) where y _ t represents the observed value at t time point and k represents the size of the moving average window. Example:
Suppose you want to predict the daily temperature change in the next month, and you have collected the temperature data of the past 10 days, as shown below:
28, 29, 30, 3 1, 32, 34, 35, 33, 32, 3 1
If the moving average window size k = 3 is selected, the predicted temperature value of 1 1 day can be calculated:
y _ 1 1 =( 1/3)*(35+33+32)= 100/3≈33.33
Exponential smoothing model:
Formula:
Y _ t = α * y _ t-1+(1-α) * yhat _ t-1Here, y _ t represents the observed value at time t, and α is the smoothing constant (0
Example:
Suppose you want to predict the daily sales change of next month based on the sales data of the past 10 days, as follows:
100, 1 10, 105, 120, 1 15, 125, 130, 135, 140, 150
If the smoothing constant α = 0.2 is selected, the sales forecast value of 1 1 day can be calculated:
y _ 1 1 = 0.2 * 150+0.8 * 140 = 30+ 1 12 = 142