With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1.

Week |
Sales (1000s of gallons) |

1 | 17 |

2 | 21 |

3 | 19 |

4 | 23 |

5 | 18 |

6 | 16 |

7 | 20 |

8 | 18 |

9 | 22 |

10 | 20 |

11 | 15 |

12 | 22 |

- Applying the MSE measure of forecast accuracy, would you prefer
a smoothing constant of = 0.1 or =
0.2 for the gasoline sales time series? Do not round your
interim computations and round your final answers to two decimal
places.

**= 0.1****= 0.2**MSE

- Are the results the same if you apply MAE as the measure of
accuracy? Do not round your interim computations and round your
final answers to two decimal places.

**= 0.1****= 0.2**MAE

- What are the results if MAPE is used? Do not round your interim
computations and round your final answers to two decimal
places.

**= 0.1****= 0.2**MAPE

Community Answer

a. {:[14quad" Alpha "=0.1quad" Alpha "=0.2],[15" MSE "],[9.25],[8.98]:} Alpha = 0.2 ... See the full answer