FIGURE 1. Observed Historical Data Month (t) Demand A(t) Forecast f(t) with n = 3 Forecast f(t) with n = 5 1 6 2 7 3 5 4 7 (6 + 7 + 5) ÷ 3 = 6 5 8 (7 + 5 + 7) ÷ 3 = 6.33 6 9 (5 + 7 + 8) ÷ 3 = 6.67 (6 + 7 + 5 + 7 + 8) ÷ 5 = 6.6 7 3 (7 +8 + 9) ÷ 3 = 8 (7 + 5 + 7 + 8 + 9) ÷ 5 = 7.2 8 4 (8 + 9 + 3) ÷ 3 = 6.67 (5 + 7 + 8 + 9 + 3) ÷ 5 = 6.4 9 5 (9 + 3 + 4) ÷ 3 = 5.33 (7 + 8 + 9 + 3 + 4) ÷ 5 = 6.2 10 5 (3 + 4 + 5) ÷ 3 = 4 (8 + 9 + 3 + 4 + 5) ÷ 5 = 5.8 TABLE 1. Moving Average with n = 3 and n = 5 FIGURE 2. Moving Average with n = 3 and n = 5 Exponential Smoothing without a Trend Exponential smoothing without a trend is another forecasting method that could be applied to observed data that shows no trend like the one in Figure 1. The key difference between exponential smoothing without a trend and moving average is that exponential smoothing has a self-adjusting mechanism that would adjust previous forecast error. On top of that, older data is out-weighted by more recent data in this method, assuming that the more recent data has a greater link to the future forecast comparing to the older ones (5). In the equation below, F(t) represents the smooth
FIGURE 1. Observed Historical Data Month (t) Demand A(t) Forecast f(t) with n = 3 Forecast f(t) with n = 5 1 6 2 7 3 5 4 7 (6 + 7 + 5) ÷ 3 = 6 5 8 (7 + 5 + 7) ÷ 3 = 6.33 6 9 (5 + 7 + 8) ÷ 3 = 6.67 (6 + 7 + 5 + 7 + 8) ÷ 5 = 6.6 7 3 (7 +8 + 9) ÷ 3 = 8 (7 + 5 + 7 + 8 + 9) ÷ 5 = 7.2 8 4 (8 + 9 + 3) ÷ 3 = 6.67 (5 + 7 + 8 + 9 + 3) ÷ 5 = 6.4 9 5 (9 + 3 + 4) ÷ 3 = 5.33 (7 + 8 + 9 + 3 + 4) ÷ 5 = 6.2 10 5 (3 + 4 + 5) ÷ 3 = 4 (8 + 9 + 3 + 4 + 5) ÷ 5 = 5.8 TABLE 1. Moving Average with n = 3 and n = 5 FIGURE 2. Moving Average with n = 3 and n = 5 Exponential Smoothing without a Trend Exponential smoothing without a trend is another forecasting method that could be applied to observed data that shows no trend like the one in Figure 1. The key difference between exponential smoothing without a trend and moving average is that exponential smoothing has a self-adjusting mechanism that would adjust previous forecast error. On top of that, older data is out-weighted by more recent data in this method, assuming that the more recent data has a greater link to the future forecast comparing to the older ones (5). In the equation below, F(t) represents the smooth
Hoang_-_Time_Series_Forecasting.docx