uppose a four period weighted average is being used to forecast demand. Weights for the periods are: t-4 = 0.05 t-3 = 0.15 t-2 = 0.35 t-1 = 0.45 Demand observed in the previous four periods was: t-4 = 9,957 t-3 = 9,760 t-2 = 10,492 t-1 = 9,450 What will be the demand forecast for period t? (Keep one decimal place in your answer).

uppose a four period weighted average is being used to forecast demand. Weights for the periods are: t-4 = 0.05 t-3 = 0.15 t-2 = 0.35 t-1 = 0.45 Demand observed in the previous four periods was: t-4 = 9,957 t-3 = 9,760 t-2 = 10,492 t-1 = 9,450 What will be the demand forecast for period t? (Keep one decimal place in your answer).

Essentials of Business Analytics (MindTap Course List)

2nd Edition

ISBN:9781305627734

Author:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Publisher:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Chapter8: Time Series Analysis And_forecasting

Section: Chapter Questions

Problem 4P: Consider the following time series data: Compute MSE using the most recent value as the forecast...

See similar textbooks

Similar questions

Suppose a four period weighted average is being used to forecast demand. Weights for the periods are: Wt-4 = 0.1 Wt-3 = 0.2 Wt-2 = 0.35 Wt-1 = 0.35 Demand observed in the previous four periods was: At-4 = 15,781 At-3 = 14,539 At-2 = 14,503 At-1 = 14,579 What will be the demand forecast for period t? (Keep one decimal place in your answer).
Assume an initial starting Ft of 300 units, a trend (Tt ) of eight units, an alpha of 0.30, and a delta of 0.40. If actual demand turned out to be 288, calculate the forecast for the next period.
The variance ratio (VR) can be used to determine whether returns satisfy the efficient market hypothesis (EMH). Let n = 5 and r5,t be the five period log return at time t, where t = 1, 2,..., T. 1. Express (r5,t — Ã5) in terms of deviations of the relevant one period log returns from their respective means. Denote 75 to be the mean of the five period log return.
Consider the following five monthly returns: 0.04 −0.03 0.03 0.07 −0.02 a. Calculate the arithmetic average monthly return over this period. b. Calculate the geometric average monthly return over this period. c. Calculate the monthly variance over this period. d. Calculate the monthly standard deviation over this period.
The Weekly Demand for a certain commodity has been recorded as follows: Week 1 2 3 4 5 6 7 8 9 Demand 800 1400 1000 1500 1500 1300 1800 1700 1300 Week 10 11 12 13 14 15 16 17 18 Demand 1700 1500 2300 2300 2000 2000 2100 2200 2300 Using a Naïve Method, determine the Forecast for Week 19 Using the Moving average with a 3-week Forecast, what is the demand for Week 19. Using the moving average with a 9-week Forecast, what is the demand for Week 19. State which forecast is best on the result for a., b., and c. forecasts. Explain your answer. Use the exponential smoothing technique with an α = 0.1 to determine the forecast for Week 19. Use the exponential smoothing technique with an α = 0.3 to determine the forecast for Week 19. Which forecast on e., and f. is better? Explain. Use the Least Square Method to determine the forecast for Week 19.
Consider the following time series data: a. Compute MSE using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. Compute MSE using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?
Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Develop a three-week moving average for this time series. Compute MSE and a forecast for week 8. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 8. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year.
Use the following returns for X and Y. Returns Year X Y 1 22.7 % 29.1 % 2 – 17.7 – 4.7 3 10.7 31.1 4 21.4 – 16.4 5 5.7 35.1 a. Calculate the average returns for X and Y. (Do not round intermediate calculations and enter your answers as a percent rounded to 2 decimal places, e.g., 32.16.) b. Calculate the variances for X and Y. (Do not round intermediate calculations and round your answers to 6 decimal places, e.g., 32.161616.) c. Calculate the standard deviations for X and Y. (Do not round intermediate calculations and enter your answers as a percent rounded to 2 decimal places, e.g., 32.16.)
The Simple Exponential Smoothing (SES) forecast with an alpha of 25 for period 4 is: (round to 2 decimal places) Absolute Absolute Percent Error Squared Error Naive X(Period) Y(Sales) Forecast Error Error 14 15 3 15 41 10 15 12 Your Answer:
The expected price of an in one year is $75 and its variance (σ 2) is 0.64. The current price of the asset is $65. The Phibor being used as a reference rate is 7%. The value to the short and to the long would be?
1)Find the forecast for 2012 using the time series trend method. Period Demand 2007 200 2008 150 2009 125 2010 100 2011 50 2) Find the forecast for all possible periods N=3, using the data and the moving average method.