Forecasting

Forecasting Product Demand in R

Aric LaBarr, Ph.D.

Senior Data Scientist, Elder Research

Forecasting

Goal of most time series models!
Models use past values or "shocks" to predict the future
Pattern recognition followed by pattern repetition

Forecasting

Forecasting Example

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")

How to Evaluate Forecasts?

2 Common Measures of Accuracy:
1. Mean Absolute Error (MAE) $$ \frac {1}{n} \sum_{i=1}^n |Y_t - \hat{Y}_t| $$
2. Mean Absolute Percentage Error (MAPE) $$ \frac {1}{n} \sum_{i=1}^n | \frac {Y_t - \hat{Y}_t} {Y_t} | \times 100 $$

MAE and MAPE Example

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

Let's practice!

Forecasting Product Demand in R