Forecast Accuracy Using Naive Method

Forecast Accuracy Using Naive Method

Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy.

```{r naive-method, echo=TRUE # Time Series Data weeks <- 1:6 values <- c(17, 13, 15, 11, 17, 14)} `````

Naive Forecasting

```{forecasts <- c(NA, head(values, -1)) # Forecast for week 2-6 based on week 1-5 }

Errors

```{ errors <- forecasts - values # Forecast Error absolute_errors <- abs(errors) # Absolute Error squared_errors <- errors^2 # Squared Error percentage_errors <- abs(errors) / values * 100 # Percentage Error }

Calculating Forecast Accuracy Measures

```{ mae <- mean(absolute_errors, na.rm = TRUE) # Mean Absolute Error mse <- mean(squared_errors, na.rm = TRUE) # Mean Squared Error mape <- mean(percentage_errors, na.rm = TRUE) # Mean Absolute Percentage Error }

Forecast for Week 7

```{ forecast_week_7 <- tail(values, 1) }

Output Results

{ list( MAE = mae, MSE = mse, MAPE = mape, Forecast_Week_7 = forecast_week_7 ) }