(a) Construct a time series plot. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 6 to 18 on the vertical axis. The plot reaches its maximum time series value at month 1. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 11 to 23 on the vertical axis. The plot reaches its maximum time series value at month 7. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 6 to 18 on the vertical axis. The plot reaches its maximum time series value at month 7. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 11 to 23 on the vertical axis. The plot reaches its maximum time series value at month 1. What type of pattern exists in the data? The data appear to follow a seasonal pattern.The data appear to follow a horizontal pattern. The data appear to follow a trend pattern.The data appear to follow a cyclical pattern. (b) Develop the three-month moving average forecasts for this time series. Month Time Series Value Forecast 1 23 2 12 3 19 4 11 5 18 6 22 7 14 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for month 8? (c) Use ? = 0.2 to compute the exponential smoothing forecasts for the time series. (Round your answers to two decimal places.) Month Time Series Value Forecast 1 23 2 12 3 19 4 11 5 18 6 22 7 14 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for month 8? (Round your answer to two decimal places.) (d) Compare the three-month moving average approach with the exponential smoothing approach using ? = 0.2. Which appears to provide more accurate forecasts based on MSE? The three-month moving average provides a better forecast since it has a larger MSE than the exponential smoothing using ? = 0.2.The exponential smoothing using ? = 0.2 provides a better forecast since it has a smaller MSE than the three-month moving average. The three-month moving average provides a better forecast since it has a smaller MSE than the exponential smoothing using ? = 0.2.The exponential smoothing using ? = 0.2 provides a better forecast since it has a larger MSE than the three-month moving average.

(a) Construct a time series plot. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 6 to 18 on the vertical axis. The plot reaches its maximum time series value at month 1. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 11 to 23 on the vertical axis. The plot reaches its maximum time series value at month 7. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 6 to 18 on the vertical axis. The plot reaches its maximum time series value at month 7. A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 11 to 23 on the vertical axis. The plot reaches its maximum time series value at month 1. What type of pattern exists in the data? The data appear to follow a seasonal pattern.The data appear to follow a horizontal pattern. The data appear to follow a trend pattern.The data appear to follow a cyclical pattern. (b) Develop the three-month moving average forecasts for this time series. Month Time Series Value Forecast 1 23 2 12 3 19 4 11 5 18 6 22 7 14 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for month 8? (c) Use ? = 0.2 to compute the exponential smoothing forecasts for the time series. (Round your answers to two decimal places.) Month Time Series Value Forecast 1 23 2 12 3 19 4 11 5 18 6 22 7 14 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for month 8? (Round your answer to two decimal places.) (d) Compare the three-month moving average approach with the exponential smoothing approach using ? = 0.2. Which appears to provide more accurate forecasts based on MSE? The three-month moving average provides a better forecast since it has a larger MSE than the exponential smoothing using ? = 0.2.The exponential smoothing using ? = 0.2 provides a better forecast since it has a smaller MSE than the three-month moving average. The three-month moving average provides a better forecast since it has a smaller MSE than the exponential smoothing using ? = 0.2.The exponential smoothing using ? = 0.2 provides a better forecast since it has a larger MSE than the three-month moving average.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Consider the following time series data.

Month	1	2	3	4	5	6	7
Value	23	12	19	11	18	22	14

(a)

Construct a time series plot.

A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 6 to 18 on the vertical axis. The plot reaches its maximum time series value at month 1.

A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 11 to 23 on the vertical axis. The plot reaches its maximum time series value at month 7.

A time series plot contains a series of 7 points connected by line segments. The horizontal axis ranges from 0 to 8 and is labeled: Month. The vertical axis ranges from 0 to 30 and is labeled: Time Series Value. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 11 to 23 on the vertical axis. The plot reaches its maximum time series value at month 1.

What type of pattern exists in the data?

The data appear to follow a seasonal pattern.The data appear to follow a horizontal pattern. The data appear to follow a trend pattern.The data appear to follow a cyclical pattern.

(b)

Develop the three-month moving average forecasts for this time series.

Month	Time Series Value	Forecast
1	23
2	12
3	19
4	11
5	18
6	22
7	14

Compute MSE. (Round your answer to two decimal places.)

MSE =

What is the forecast for month 8?

(c)

Use ? = 0.2 to compute the exponential smoothing forecasts for the time series. (Round your answers to two decimal places.)

Month	Time Series Value	Forecast
1	23
2	12
3	19
4	11
5	18
6	22
7	14

Compute MSE. (Round your answer to two decimal places.)

MSE =

What is the forecast for month 8? (Round your answer to two decimal places.)

(d)

Compare the three-month moving average approach with the exponential smoothing approach using

? = 0.2.

Which appears to provide more accurate forecasts based on MSE?

The three-month moving average provides a better forecast since it has a larger MSE than the exponential smoothing using ? = 0.2.The exponential smoothing using ? = 0.2 provides a better forecast since it has a smaller MSE than the three-month moving average. The three-month moving average provides a better forecast since it has a smaller MSE than the exponential smoothing using ? = 0.2.The exponential smoothing using ? = 0.2 provides a better forecast since it has a larger MSE than the three-month moving average.

Consider the following time series data.
Month
1 2
3
4
5
6
7
Value 23
19| 11
18
22
14
(a) Construct a time series plot.
30 1
30
30
30
25
25
25
25
20
20-
20
15
15
15
15
10
10
10
10
5-
5
5
1
0 1
5
7
0 1
5
3
6
2
3
4.
6
8
1
2
3
4
6
8
3
4
6
Month
Month
Month
Month
What type of pattern exists in the data?
O The data appear to follow a seasonal pattern.
O The data appear to follow a horizontal pattern.
O The data appear to follow a trend pattern.
O The data appear to follow a cyclical pattern.
(b) Develop the three-month moving average forecasts for this time series.
Time Series
Value
Month
Forecast
1
23
2
12
19
4
11
18
6
22
7
14
Compute MSE. (Round your answer to two decimal places.)
MSE =|
What is the forecast for month 8?
(c) Use a = 0.2 to compute the exxponential smoothing forecasts for the time series. (Round your answers to two decimal places.)
Time Series
Value
Month
Forecast
23
2
12
3
19
4
11
5
18
6
22
7
14
Compute MSE. (Round your answer to two decimal places.)
MSE =
What is the forecast for month 8? (Round your answer to two decimal places.)

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