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Which of the above forecasts will have the highest magnitude in actual (absolute) terms? a. 3-period moving average B.Simple Moving Average C. (6,4,3,2) D. Alpha = .3

Which of the above forecasts will have the highest magnitude in actual (absolute) terms? a. 3-period moving average B.Simple Moving Average C. (6,4,3,2) D. Alpha = .3

MATLAB: An Introduction with Applications
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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Which of the above forecasts will have the highest magnitude in actual (absolute) terms?

a. 3-period moving average
B.Simple Moving Average
C. (6,4,3,2)
D. Alpha = .3
### Forecasting Method Comparison Table The following table presents the error metrics for various forecasting methods. The methods are compared using four different error metrics: RSFE, MFE, MAPE, and SDFE. | **Method** | **(6,4,3,2)** | **3P avg** | **Alpha 0.3** | **SMA** | |--------------|--------------|-------------|---------------|---------| | **RSFE** | -26 | -15 | -2 | -13 | | **MFE** | -6.5 | -3 | -0.4 | -2.1 | | **MAPE** | 16.22 | 12.11 | 14.21 | 15.3 | | **SDFE** | 1.5 | 11.26 | 11.3 | 12.2 | **Key:** - **RSFE (Running Sum of Forecast Errors)**: This indicates the accumulated sum of errors in the forecast. - **MFE (Mean Forecast Error)**: This measures the average forecast error over a period. - **MAPE (Mean Absolute Percentage Error)**: This indicates the average absolute percentage error of the forecasts. - **SDFE (Standard Deviation of Forecast Error)**: This measures the dispersion or variability in forecast error values. **Explanation of Methods:** 1. **(6,4,3,2)**: Possibly a weighted moving average with respective weights. 2. **3P avg**: Three-period moving average. 3. **Alpha 0.3**: Exponential smoothing with a smoothing constant (alpha) of 0.3. 4. **SMA**: Simple Moving Average. This table helps in understanding and comparing the accuracy of different forecasting methods based on various error metrics, aiding in selecting the most appropriate method for a given dataset.
Transcribed Image Text:### Forecasting Method Comparison Table The following table presents the error metrics for various forecasting methods. The methods are compared using four different error metrics: RSFE, MFE, MAPE, and SDFE. | **Method** | **(6,4,3,2)** | **3P avg** | **Alpha 0.3** | **SMA** | |--------------|--------------|-------------|---------------|---------| | **RSFE** | -26 | -15 | -2 | -13 | | **MFE** | -6.5 | -3 | -0.4 | -2.1 | | **MAPE** | 16.22 | 12.11 | 14.21 | 15.3 | | **SDFE** | 1.5 | 11.26 | 11.3 | 12.2 | **Key:** - **RSFE (Running Sum of Forecast Errors)**: This indicates the accumulated sum of errors in the forecast. - **MFE (Mean Forecast Error)**: This measures the average forecast error over a period. - **MAPE (Mean Absolute Percentage Error)**: This indicates the average absolute percentage error of the forecasts. - **SDFE (Standard Deviation of Forecast Error)**: This measures the dispersion or variability in forecast error values. **Explanation of Methods:** 1. **(6,4,3,2)**: Possibly a weighted moving average with respective weights. 2. **3P avg**: Three-period moving average. 3. **Alpha 0.3**: Exponential smoothing with a smoothing constant (alpha) of 0.3. 4. **SMA**: Simple Moving Average. This table helps in understanding and comparing the accuracy of different forecasting methods based on various error metrics, aiding in selecting the most appropriate method for a given dataset.
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