Question
In: Math
Week 1 2 3 4 5 6 Value 18 14 17 12 18 15 Calculate the...
| Week | 1 | 2 | 3 | 4 | 5 | 6 |
| Value | 18 | 14 | 17 | 12 | 18 | 15 |
Calculate the measures of forecast error using the naive (most recent value) method and the average of historical data (to 2 decimals).
| Naive method | Historical data | |
| Mean absolute error | ||
| Mean squared error | ||
| Mean absolute percentage error |
Solutions
milcah answered 1 year agoRelated Solutions
Week 1 2 3 4 5 6 Value 18 14 15 10 17 15 Using the...
Week
1
2
3
4
5
6
Value
18
14
15
10
17
15
Using the naïve method (most recent value) as the forecast for
the next week, compute the following measures of forecast
accuracy.
(a)
Mean absolute error
If required, round your answer to one decimal place.
(b)
Mean squared error
If required, round your answer to one decimal place.
(c)
Mean absolute percentage error
If required, round your intermediate calculations and final
answer to two decimal places.
(d)...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 14...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
14
16
11
17
15
Using the naïve method (most recent value) as the forecast for
the next week, compute the following measures of forecast
accuracy:
Mean absolute error (MAE)
Mean squared error (MSE)
Mean absolute percentage error (MAPE)
Using the average of all the historical data as a forecast for
the next period, compute the same three values. Round your answers
to two decimal...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 12...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
12
16
10
17
15
(a) Construct a time series plot.
What type of pattern exists in the data? The data appear to
follow a horizontal pattern.
(b) Develop the three-week moving average forecasts for this
time series. (Round your answers to two decimal places.)
Week
Time Series
Value
Forecast
1
18
2
12
3
16
4
10
15.33
5
17
12.67
6
15
14.33...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 12...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
12
16
10
17
15
Develop the three-week moving average forecasts for this time
series. (Round your answers to two decimal places.)
Week
Time Series
Value
Forecast
1
18
2
12
3
16
4
10
5
17
6
15
Compute MSE. (Round your answer to two decimal places.)
MSE =
What is the forecast for week 7?
Use α = 0.2 to compute the exponential...
Periods 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 12% 14% 15% 16% 18%...
Periods
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
12%
14%
15%
16%
18%
20%
1
0.990
0.980
0.971
0.962
0.952
0.943
0.935
0.926
0.917
0.909
0.893
0.877
0.870
0.862
0.847
0.833
2
0.980
0.961
0.943
0.925
0.907
0.890
0.873
0.857
0.842
0.826
0.797
0.769
0.756
0.743
0.718
0.694
3
0.971
0.942
0.915
0.889
0.864
0.840
0.816
0.794
0.772
0.751
0.712
0.675
0.658
0.641
0.609
0.579
4
0.961
0.924
0.888
0.855
0.823
0.792
0.763
0.735
0.708
0.683
0.636...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
13
16
11
17
14
a. Use a smoothing constant of a = 0.4 to
compute the MSE (to 2 decimals).
(___)
2.Consider the following time series data.
Week
1
2
3
4
5
6
Value
19
13
17
12
17
15
b. Develop the three-week moving average
forecasts for this time series. Compute MSE and a forecast for week
7 (to 2 decimals if...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
13
17
11
17
15
Calculate the measures of forecast error using the naive (most
recent value) method and the average of historical data (to 2
decimals).
Naive method
Historical data
Mean absolute error
Mean squared error
Mean absolute percentage error
Top of Form
Consider the following time series
data.
Month
1
2
3
4
5
6
7
Value
23
14
19
10
18...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
13
17
12
17
15
a. develop the three-week moving average forecasts for this time
series. Compute MSE and a forecast for week 7 (to 2 decimals if
necessary).
b. use = 0.2 to compute the exponential smoothing forecasts for
the time series. Compute MSE and a forecast for week 7 (to 2
decimals).
c. use a smoothing constant of =0.4 to compute the MSE...
Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13...
Consider the following time series data. Week 1 2 3 4 5 6 Value
18 13 17 12 17 15 a. Which of the following is a correct time
series plot for this data? What type of pattern exists in the data?
b. Develop the three-week moving average forecasts for this time
series. Compute MSE and a forecast for week (to 2 decimals if
necessary). MSE The forecast for week c. Use to compute the
exponential smoothing forecasts for the...
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 12...
Consider the following time series data.
Week
1
2
3
4
5
6
Value
19
12
15
11
18
13
a. Use α = 0.2 to compute the exponential smoothing
forecasts for the time series.
Week
Time Series
Value
Forecast
1
19
2
12
3
15
4
11
5
18
6
13
Compute MSE. (Round your answer to two decimal places.)
MSE =
b. Use a smoothing constant of α = 0.4 to compute the
exponential smoothing forecasts.
Week
Time...
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