การพยากรณ์ความต้องการสินค้าใน R
Aric LaBarr, Ph.D.
Senior Data Scientist, Elder Research
forecast_M_t <- forecast(M_t_model, h = 22) for_dates <- seq(as.Date("2017-01-01"), length = 22, by = "weeks") for_M_t_xts <- xts(forecast_M_t$mean, order.by = for_dates) plot(M_t_valid, main = 'Forecast Comparison') lines(for_M_t_xts, col = "blue")
forecast_M_t <- forecast(M_t_model, h = 22)
for_dates <- seq(as.Date("2017-01-01"), length = 22, by = "weeks") for_M_t_xts <- xts(forecast_M_t$mean, order.by = for_dates)
plot(M_t_valid, main = 'Forecast Comparison') lines(for_M_t_xts, col = "blue")
ตัววัดความแม่นยำที่ใช้บ่อย 2 ตัว:
ค่าเฉลี่ยความคลาดเคลื่อนสัมบูรณ์ (MAE) $$ \frac {1}{n} \sum_{i=1}^n |Y_t - \hat{Y}_t| $$
ค่าเฉลี่ยความคลาดเคลื่อนสัมบูรณ์เป็นเปอร์เซ็นต์ (MAPE) $$ \frac {1}{n} \sum_{i=1}^n | \frac {Y_t - \hat{Y}_t} {Y_t} | \times 100 $$
for_M_t <- as.numeric(forecast_M_t$mean) v_M_t <- as.numeric(M_t_valid) MAE <- mean(abs(for_M_t - v_M_t)) MAPE <- 100*mean(abs((for_M_t - v_M_t)/v_M_t)) print(MAE)
for_M_t <- as.numeric(forecast_M_t$mean) v_M_t <- as.numeric(M_t_valid)
MAE <- mean(abs(for_M_t - v_M_t)) MAPE <- 100*mean(abs((for_M_t - v_M_t)/v_M_t))
print(MAE)
[1] 198.7976
print(MAPE)
[1] 9.576247
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