The model that assumes that the actual time series value Yt is the product of a...
The model that assumes that the actual time series value Yt is the product of a trend-cycle, season, and error component is
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additive Holt-Winters model |
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weighted moving average model |
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linear trend regression model |
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Holt linear trend model |
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purely additive time series model |
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purely multiplicative time series model |
Homework Answers
The purely multiplicative time series model assumes that the actual time series data is the product of its various components.
The time series data has four components : trend cycle, the seasonal and the random error component.
The purely multiplicative model assumes the actual time series data to be the product of trend cycle , season and error component.
Add Answer to:
The model that assumes
that the actual time series value Yt is the
product of a...
A) Discuss what the time series decomposition tells you about your data series. Include discussio...
a) Discuss what the time series decomposition tells you about
your data series. Include discussion of the seasonal, cyclical, and
trend components.
b) Compare the time series decomposition forecasts with Holt
Winters. Within the sample, is the times series decomposition or
Holt Winters more accurate? Try to explain why. (see below for
data)
Audit Trail- Statistics Accuracy Measures MAPE R-Square Value 1.65% 98.99% Forecast Statistics Mean Standard Deviation Value 5.06 1.04 Method Statistics Method Selected Basic Method Decomposition type Value...
In time series data, linear regression allows to incorporate in the model... (a) a linear time...
In time series data, linear regression allows to incorporate in the model... (a) a linear time trend (b) an exponential time trend (c) a quadratic time trend (d) all of the previous
The accompanying data file contains 20 observations for t and yt. Actual series are plotted along with the superimposed linear and exponential trends. t y t y t y t y 1 1.91 6 4.93...
The accompanying data file contains 20 observations for
t and yt.
Actual series are plotted along with the superimposed linear and
exponential trends.
t
y
t
y
t
y
t
y
1
1.91
6
4.93
11
5.96
16
15.58
2
3.57
7
6.78
12
9.02
17
12.33
3
5.83
8
4.58
13
9.52
18
13.95
4
5.39
9
7.19
14
14.02
19
15.63
5
2.78
10
8.81
15
14.57
20
19.77
The accompanying data file contains 20 observations for tand...
Q7 We have observed the time series shown in the following line chart: 60 50 40...
Q7 We have observed the time series shown in the following line chart: 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time Which would be the most appropriate forecasting model to use for this time series? Select one: a. Regression b. Single exponential smoothing O c. Holt-Winters (including both trend and seasonality) O d. Double exponential smoothing
Which of the following is TRUE (check all the correct choices, wrong answers will incur negative...
Which of the following is TRUE (check all the correct choices, wrong answers will incur negative marking To find MAPE, you need to find the absolute difference between the actual value and the forecast, then divide this absolute difference by the forecast value. Then take the average of all these absolute percentage errors. To find MAD, you need to find the difference between the actual value and the forecast and then take the average of all these differences To find...
(1) Explain smoothing methods in time series. How to find the opti- mal value of the...
(1) Explain smoothing methods in time series. How to find the opti- mal value of the smoothing parameter in an exponentially weighted moving average. (2) Prove that ARCH(1) process is leptokurtic. Also establish its equiv- alence to AR process. (3) Prove that exponentially weighted moving average model is a special case of GARCH(1,1) processes. Also establish its equivalence to ARMA(1,1) process. (4) Find the minimum mean square error (MMSE) forecast of AR(2) process and then obtain the variance of the...
If you model a time series Yt using a stationary ARMA process with a nonzero constant...
If you model a time series Yt using a stationary ARMA process with a nonzero constant (µ unequal to 0) and use it to forecast future values of Yt, then as you forecast further and further into the future, the confidence interval widths for your forecasts will (a) continue to increase and eventually reach arbitrarily large values. (b) gradually decay to zero. (c) cutoff to zero after some lag. (d) converge to a non-zero limiting value.
Consider the following time series. t 1 23 4 5 6 7 Yt 83 60 45 36 31 30 34 a. Which of the follow...
Consider the following time series. t 1 23 4 5 6 7 Yt 83 60 45 36 31 30 34 a. Which of the following is the correct time series plot? TimeSeriesValue 90 80 70 60 50 40 20 10 TimePeriodit 2 I imeSeriesValue 90 80 70 60 50 40 20 10 2 TimePeriodit 3. TimeSeries Value 90 80 60 50 40 30 10 TimePeriodit 4 TimeSeries Value 90 80 70 60 50 40 30 10 2 TimePeriod t -...
Which of the following is NOT a time-series model? a. exponential smoothing b. naive approach c....
Which of the following is NOT a time-series model? a. exponential smoothing b. naive approach c. multiple regression d. moving average
17) The following linear trend expression was estimated using a time series with 17 time periods...
17) The following linear trend expression was estimated using a time series with 17 time periods (that is, the values of tare 1, 2, V,= 129.23.8t Calculate a 95% prediction interval for the value of Y at time period t 18 (i.e, h 1 period ahead). Use the fact that the average of the time values is 9, the standard deviation of the time values is s 5.05, and the regression standard error se 0.841. Take all calculations and the...
a) Discuss what the time series decomposition tells you about
your data series. Include discussion of the seasonal, cyclical, and
trend components.
b) Compare the time series decomposition forecasts with Holt
Winters. Within the sample, is the times series decomposition or
Holt Winters more accurate? Try to explain why. (see below for
data)
Audit Trail- Statistics Accuracy Measures MAPE R-Square Value 1.65% 98.99% Forecast Statistics Mean Standard Deviation Value 5.06 1.04 Method Statistics Method Selected Basic Method Decomposition type Value...
In time series data, linear regression allows to incorporate in the model... (a) a linear time trend (b) an exponential time trend (c) a quadratic time trend (d) all of the previous
The accompanying data file contains 20 observations for
t and yt.
Actual series are plotted along with the superimposed linear and
exponential trends.
t
y
t
y
t
y
t
y
1
1.91
6
4.93
11
5.96
16
15.58
2
3.57
7
6.78
12
9.02
17
12.33
3
5.83
8
4.58
13
9.52
18
13.95
4
5.39
9
7.19
14
14.02
19
15.63
5
2.78
10
8.81
15
14.57
20
19.77
The accompanying data file contains 20 observations for tand...
Q7 We have observed the time series shown in the following line chart: 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time Which would be the most appropriate forecasting model to use for this time series? Select one: a. Regression b. Single exponential smoothing O c. Holt-Winters (including both trend and seasonality) O d. Double exponential smoothing
Which of the following is TRUE (check all the correct choices, wrong answers will incur negative marking To find MAPE, you need to find the absolute difference between the actual value and the forecast, then divide this absolute difference by the forecast value. Then take the average of all these absolute percentage errors. To find MAD, you need to find the difference between the actual value and the forecast and then take the average of all these differences To find...
(1) Explain smoothing methods in time series. How to find the opti- mal value of the smoothing parameter in an exponentially weighted moving average. (2) Prove that ARCH(1) process is leptokurtic. Also establish its equiv- alence to AR process. (3) Prove that exponentially weighted moving average model is a special case of GARCH(1,1) processes. Also establish its equivalence to ARMA(1,1) process. (4) Find the minimum mean square error (MMSE) forecast of AR(2) process and then obtain the variance of the...
Consider the following time series. t 1 23 4 5 6 7 Yt 83 60 45 36 31 30 34 a. Which of the following is the correct time series plot? TimeSeriesValue 90 80 70 60 50 40 20 10 TimePeriodit 2 I imeSeriesValue 90 80 70 60 50 40 20 10 2 TimePeriodit 3. TimeSeries Value 90 80 60 50 40 30 10 TimePeriodit 4 TimeSeries Value 90 80 70 60 50 40 30 10 2 TimePeriod t -...
17) The following linear trend expression was estimated using a time series with 17 time periods (that is, the values of tare 1, 2, V,= 129.23.8t Calculate a 95% prediction interval for the value of Y at time period t 18 (i.e, h 1 period ahead). Use the fact that the average of the time values is 9, the standard deviation of the time values is s 5.05, and the regression standard error se 0.841. Take all calculations and the...
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