Campus Advising needs to allocate time this week for office appointments, so she needs to forecast the number of students who will seek appointments The advisor has gathered the following time series data recently. Period Student Appointments 4 weeks ago 95 3 weeks ago 80 2 weeks ago 65 Last week 50 on thie formation
Campus Advising needs to allocate time this week for office appointments, so she needs to forecast the number of students who will seek appointments The advisor has gathered the following time series data recently. Period Student Appointments 4 weeks ago 95 3 weeks ago 80 2 weeks ago 65 Last week 50 on thie formation
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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![**Forecasting Student Appointments Using Exponential Smoothing**
Campus Advising needs to allocate time this week for office appointments, so they need to forecast the number of students who will seek appointments. The advisor has gathered the following time series data recently:
| Period | Student Appointments |
|---------------|----------------------|
| 4 weeks ago | 95 |
| 3 weeks ago | 80 |
| 2 weeks ago | 65 |
| Last week | 50 |
Answer the next two questions based on this information.
**Question:**
What would be this week's forecast for student appointments using exponential smoothing with an alpha of 0.2, if the forecast for two weeks ago was 90?
- A. 65
- B. 52
- C. 50
- D. 78
- E. 85
**Explanation:**
Exponential smoothing is a time series forecasting method for univariate data. The forecast is typically calculated using the formula:
\[ F_t = \alpha \times A_{t-1} + (1-\alpha) \times F_{t-1} \]
where:
- \( F_t \) is the forecast for the current period,
- \( \alpha \) is the smoothing constant (between 0 and 1),
- \( A_{t-1} \) is the actual value from the previous period,
- \( F_{t-1} \) is the forecast for the previous period.
In this case, use the data provided to compute the forecast for the current week.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F69849748-7830-4cfb-8d66-81e363e16687%2F1e127303-5667-4a5a-8683-63812b6712e0%2F6e8dqe_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Forecasting Student Appointments Using Exponential Smoothing**
Campus Advising needs to allocate time this week for office appointments, so they need to forecast the number of students who will seek appointments. The advisor has gathered the following time series data recently:
| Period | Student Appointments |
|---------------|----------------------|
| 4 weeks ago | 95 |
| 3 weeks ago | 80 |
| 2 weeks ago | 65 |
| Last week | 50 |
Answer the next two questions based on this information.
**Question:**
What would be this week's forecast for student appointments using exponential smoothing with an alpha of 0.2, if the forecast for two weeks ago was 90?
- A. 65
- B. 52
- C. 50
- D. 78
- E. 85
**Explanation:**
Exponential smoothing is a time series forecasting method for univariate data. The forecast is typically calculated using the formula:
\[ F_t = \alpha \times A_{t-1} + (1-\alpha) \times F_{t-1} \]
where:
- \( F_t \) is the forecast for the current period,
- \( \alpha \) is the smoothing constant (between 0 and 1),
- \( A_{t-1} \) is the actual value from the previous period,
- \( F_{t-1} \) is the forecast for the previous period.
In this case, use the data provided to compute the forecast for the current week.
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