in using the decomposition method, the forecast based on trend is found using the trend line....
in using the decomposition method, the forecast based on trend is found using the trend line. how is the seasonal index used to adjust this forecast based on trend
Homework Answers
The steps to develop a forecast using the decomposition methosd are:-
- compute seasonal indices using CMAs
- deseasonalize the data by dividing each number by its seasonal index
- find the equation of the trend line using the deseasonlized data
- forecast for future periods using the trend line
- Multiply the seasonal index to the trend line forecast.
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in using the decomposition method, the forecast based on trend
is found using the trend line....
The ratio-to-trend method is useful... (a) to capture the time trend (b) to adjust for seasonal...
The ratio-to-trend method is useful... (a) to capture the time trend (b) to adjust for seasonal variations (c) to capture random fluctuations (d) to control for secular cycles
please explain your answer. 2:5-34 Resolve Problem 5-33 with a = 0.3. Using MAD, which smoothing constant provides a...
please explain your answer.
2:5-34 Resolve Problem 5-33 with a = 0.3. Using MAD, which smoothing constant provides a better forecast? 3:5-35 A major source of revenue in Texas is a state sales tax on certain types of goods and services. Data are com- piled, and the state comptroller uses them to project future revenues for the state budget. One particular category of goods is classified as Retail Trade. Four years of quarterly data (in $1,000,000s) for one par- ticular...
The following table shows the actual values and forecast values calculated using a linear trend with...
The following table shows the actual values and forecast values calculated using a linear trend with seasonality model for a time series of quarterly price index: Actual value Forecast value 2018, Q1 70.0 71.3 2018, Q2 74.0 73.4 2018, Q3 76.7 74.4 2018, Q4 78.3 79.8 The Mean Absolute Deviation (MAD) for the forecast method is Answer (please round your answer to 2 decimal places).
John Taylor Salons want to forecast monthly customer demand from June through August using trend ...
Using Excel
John Taylor Salons want to forecast monthly customer demand from June through August using trend adjusted exponential smoothing. Given alpha (a) 0.20, Beta (B) -0.40, the Forecast for May 45 (FMay-45) customers, and the trend for May 0 (Tmay-0), forecast a FIT (forecast including trend) for the months of June through August. 3. Month Actual Sales May June July August 50 61 73 80 Jay Sharp Guard wants to compare the accuracy of two methods that it has...
12. Compute the forecast for Spring of 2019 using the demand data pe bain and seasonality....
12. Compute the forecast for Spring of 2019 using the demand data pe bain and seasonality. Use "decomposition using least square regrew computation. Complete the tables below. (14 points) e demand data pyes below. Assume that the data dioplays both sing least square regression technique. You may use and spreadsheets for you Season Demand De seasonalized dead Spring 2250 Summer 3050 2017 Fall 2900 Winter 5500 Spring 3750 Summer 4200 2018 Fall 3900 Winter 6600 Trend equation (or regression equation)...
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...
solve this for i1 2 3 4 using decomposition methods LU Decomposition using Method 1 (based on Gauss Elimination) 3. LU Decomposition using Method 2 (Crout's Method) 2. 24 9X1-4X2-2x3 =-16 - 3x...
solve this for i1 2 3 4 using decomposition
methods
LU Decomposition using Method 1 (based on Gauss Elimination) 3. LU Decomposition using Method 2 (Crout's Method) 2. 24 9X1-4X2-2x3 =-16 - 3x4 一4x1 + 17x2-6x3 2x16x2 +14x3-6x4 0 3x2-6x3 +14x4-18
LU Decomposition using Method 1 (based on Gauss Elimination) 3. LU Decomposition using Method 2 (Crout's Method) 2. 24 9X1-4X2-2x3 =-16 - 3x4 一4x1 + 17x2-6x3 2x16x2 +14x3-6x4 0 3x2-6x3 +14x4-18
b-2. Using the MAD method of testing the forecasting model's performance, plus actual data from 3...
b-2. Using the MAD method of testing the forecasting model's performance, plus actual data from 3 years ago through the second quarter of this year, how well did the model perform? Based on MAD, an a performs better than an a of (Click to select) c. Using the decomp (Click to select) 0.3 0.2 hod of forecasting, forecast earnings per share for the last two quarters of this year and all four quarters of next year. (Negative values should be...
A kid's toy store found that its quarterly sales of $1,000 can be adequately forecasted by...
A kid's toy store found that its quarterly sales of $1,000 can be adequately forecasted by the following multiplicative seasonal model. Trend Component: T = 150 + 35t Seasonal Index: Q1= .81 Q2= 1.09 Q3= .91 Q4= 1.19 Origin = Q4, 2012 Units (t) = Quarterly A. Forecast sales for 2019
Which of the following time series forecasting methods would not be used to forecast seasonal data?...
Which of the following time series forecasting methods would not be used to forecast seasonal data? A. dummy variable regression B. simple exponential smoothing C. time series decomposition D. multiplicative Winters method
The ratio-to-trend method is useful... (a) to capture the time trend (b) to adjust for seasonal variations (c) to capture random fluctuations (d) to control for secular cycles
please explain your answer.
2:5-34 Resolve Problem 5-33 with a = 0.3. Using MAD, which smoothing constant provides a better forecast? 3:5-35 A major source of revenue in Texas is a state sales tax on certain types of goods and services. Data are com- piled, and the state comptroller uses them to project future revenues for the state budget. One particular category of goods is classified as Retail Trade. Four years of quarterly data (in $1,000,000s) for one par- ticular...
Using Excel
John Taylor Salons want to forecast monthly customer demand from June through August using trend adjusted exponential smoothing. Given alpha (a) 0.20, Beta (B) -0.40, the Forecast for May 45 (FMay-45) customers, and the trend for May 0 (Tmay-0), forecast a FIT (forecast including trend) for the months of June through August. 3. Month Actual Sales May June July August 50 61 73 80 Jay Sharp Guard wants to compare the accuracy of two methods that it has...
12. Compute the forecast for Spring of 2019 using the demand data pe bain and seasonality. Use "decomposition using least square regrew computation. Complete the tables below. (14 points) e demand data pyes below. Assume that the data dioplays both sing least square regression technique. You may use and spreadsheets for you Season Demand De seasonalized dead Spring 2250 Summer 3050 2017 Fall 2900 Winter 5500 Spring 3750 Summer 4200 2018 Fall 3900 Winter 6600 Trend equation (or regression equation)...
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...
solve this for i1 2 3 4 using decomposition
methods
LU Decomposition using Method 1 (based on Gauss Elimination) 3. LU Decomposition using Method 2 (Crout's Method) 2. 24 9X1-4X2-2x3 =-16 - 3x4 一4x1 + 17x2-6x3 2x16x2 +14x3-6x4 0 3x2-6x3 +14x4-18
LU Decomposition using Method 1 (based on Gauss Elimination) 3. LU Decomposition using Method 2 (Crout's Method) 2. 24 9X1-4X2-2x3 =-16 - 3x4 一4x1 + 17x2-6x3 2x16x2 +14x3-6x4 0 3x2-6x3 +14x4-18
b-2. Using the MAD method of testing the forecasting model's performance, plus actual data from 3 years ago through the second quarter of this year, how well did the model perform? Based on MAD, an a performs better than an a of (Click to select) c. Using the decomp (Click to select) 0.3 0.2 hod of forecasting, forecast earnings per share for the last two quarters of this year and all four quarters of next year. (Negative values should be...
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