(c) Use aWeek23=4560.2 to compute the exponential smoothing values for the time series.Time SeriesValue16121411151316Forecast18.0820.5222.3124.86XXxCompute MSE. (Round your answer to two decimal places.)MSE=What is the forecast for week 7? (Round your answer to two decimal places.)(d) Compare the three-week moving average forecast with the exponential smoothing forecasbetter forecast based on MSE? Explain.

Solution: From the given information, α=0.2 Week Time series Value 1 16 2 12 3…

Answered: (c) Use a = 0.2 to compute the…

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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Given below is the trend line equation of a time series relating to sales of a firm. Y = 15 + 2X where 1st quarters of 1996 is the trend origin, quarterly time deviation and Y quarterly sales (in '000 Rs.). The seasonal indices of the sales were as under : I II III IV Qrs. S.I. 110 120 80 90 Basing upon the above information estimate the quarterly sales for the year 1998 by the multiplicative model of the time series.
b. Use a = 0.2 to compute the exponential smoothing forecasts for the time series (to 2 decimals). Time-Series Value Month Forecast 1 110 2 130 120 4 110 5 95 6. 125 7 140 8 140 9 105 10 80 11 100 12 110 13
1 - A simple exponential smoothing model has a current mean of 156.8. Assuming demand for next period is 170, update the value of the smoothing statistic. Let a = 0.1. 2- An electronics company has sold 145, 157, 124, 169,179, 201, 197, and 230 units of a certain stereo system quarterly for the past two years. Plot the data and fit an appropriate model. Forecast sales for the next two quarters.
3. Below you are given the first four values of a monthly time series of sales. You want to predict the sales of May based the time series data. Month Time Series Value Yt January 120 February 135 March 150 April 175
4// I swing simple exponential smoothing and the following time series data, respond to each the items. Period. Demand 1. 126 2. 112 3. 135 4. 145 5. 106 6. 101 7. 132 8. 141 9. 110 10. 131 D. Compute the MAD for each exponential smoothing forecast. ( Round your answer to 2 decimal places)
Consider the following table that gives the population of City A from Year 1 to Year 6. Complete all parts (a-k) to this question. Part 1: Fill-in the last two columns in the table. List your answers as a - j for the chart and answer the question k below. Round growth rate to the nearest ten-thousandth (4 decimal places). Population of City A, Year 1 to year 6 Year Population (P) Change ΔP{"version":"1.1","math":"<math xmlns="http://www.w3.org/1998/Math/MathML"><mo>∆</mo><mi>P</mi></math>"} Growth Rate ΔPP{"version":"1.1","math":"<math xmlns="http://www.w3.org/1998/Math/MathML"><mfrac><mrow><mo>∆</mo><mi>P</mi></mrow><mi>P</mi></mfrac></math>"} 1 315,422 N/A N/A 2 320,421 a. b. 3 321,765 c. d. 4 322,162 e. f. 5 331,165 g. h. 6 331,200 i. j. Part 2: k. Does it make sense to fit this data with an exponential model? Why or why…
Six years of quarterly data of a seasonally adjusted series are used to estimate a linear trend model as T t=181.60 + 1.44t. In addition, quarterly seasonal indices are calculated as § 1= 0.85, § 2= 0.91, Ŝ 3 = 116, and § 4= 119. a-1. Interpret the first quarterly index. In other words, what is the value of the series in the first quarter as compared to the average? 15% above O 85% below O 15% below 85% above a-2. Interpret the fourth quarterly index. In other words, what is the value of the series in the fourth quarter as compared to the average? O 81% above O 19% above 19% below O 81% below b. Make a forecast for all four quarters of next year. (Do not round intermediate calculations. Round your answers to 2 decimal places.) Quarter 1 Quarter 2 Quarter 3 Quarter 4
A statistical program is recommended. Suppose the following table shows a company's annual revenue (in billions of dollars) for 2012 (period 1) to 2017 (period 6). Year Period Sale ($ Billions) 2012 2013 2014 2015 2016 2017 1 2 3 4 5 6 1.54 1.83 2.4 3.17 3.58 4.14

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