(c) Compute the deseasonalized quarterly trend forecast for year 4. (Round your answers to three decimal places.)forecast for quarter 1forecast for quarter 2forecast for quarter 3forecast for quarter 4(d) Use the seasonal indexes to adjust the deseasonalized trend forecasts computed in part (c). (Round your answers to three decimal places.)forecast for quarter 1forecast for quarter 2forecast for quarter 3forecast for quarter 4 Consider the following time series data.Quarter Year 1112233Year Quarter4123412341(a) Deseasonalize the time series using the adjusted seasonal indexes given below. (Round your answers to three decimal places.)Time SeriesValueAdjustedSeasonal Index234423442346358766Year 296358Year 376691.2300.7340.8701.1651.2300.7340.8701.1651.2300.7340.8701.165DeseasonalizedValueT(b) Using statistical software, compute the linear trend regression equation for the deseasonalized data. (Let t = 1 denote the time series value in quarter 1 of year 1; t = 2 denote the time series value in quarter 2 of year 1; ... and t = 12 denote the time series value in quarter 4 of year 3. Round your numerical values tothree decimal places.)T₂ =

To deseasonalize the time series, we need to use the formula: Deseasonalized Value = Time Series…

Answered: Consider the following time series…

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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The president of State University wants to forecast student enrollments for this Year academic year based on the following historical data: 5 years ago 4 years ago 3 years ago 2 years ago Last year Enrollments 15.000 16.000 18.000 20.000 21.000 a- What is the forecast for this year using a 3-year weighted moving average where the weights are halved in a descending order from newest to oldest ? b. What is the forecast for this year using the trend line for these data? c. using the MAD find which forecasting technique may be appropriate for this quetion
After its move in 1990 to La Junta, Colorado, and its new initiatives, the DeBourgh Manufacturing Company began an upward climb of record sales. Suppose the figures shown here are the DeBourgh monthly sales figures from January 2001 through December 2009 (in $1,000s). a) Produce a time series plot. Are there any trends evident in the data? Does DeBourgh have a seasonal component to its sales? b) Deseasonalize the data using Multiplicative model with a 0.5 weighted moving average. Produce a time series plot of the deseasonalized data and add a trendline. c) Forecast the sales from January to December of the year 2010. d) Include a discussion of the general direction of sales and any seasonal tendencies that might be occurrinG Month 2001 2002 2003 2004 2005 2006 2007 2008 2009 January 139.7 165.1 177.8 228.6 266.7 431.8 381 431.8 495.3 February 114.3 177.8 203.2 254 317.5 457.2 406.4 444.5 533.4 March 101.6 177.8 228.6 266.7 368.3 457.2 431.8 495.3 635 April 152.4 203.2…
39- A proposed project has the following cash flow estimates: End of Year Mean Net Cash Flow Standard Deviation of Cash Flow 0 -$32,000 $1,000 1 $4,000 $2,000 2 $8,000 $3,000 3 $12,000 $5,000 4 $12,000 $6,000 5 $12,000 $7,000 Assuming independent cash flows, a normally distributed net present value, and a minimum attractive rate of return of 18 percent, determine the following:  a) The mean and standard deviation of net present value. b) The probability that the net present value is positive. c) The probability that the net present value is greater than $5,000.
The customer demand for a particular smart phone during Year 0 to Year 5 is given as 1,056,000, 1,032,000, 1,093,000, 1,024,000, 1,066,000, and 1,072,000. Using the Holt forecasting model with trend, what is the forecasted demand in year 10, where w = 0.4 and v = 0.5?a) 1,071,874b) 1,075,573c) 1,077,488d) 1,083,816
Illustrate the formulation of dynamic causal effects in time series data?
The Beaver Creek Pottery Company sells bowls and mugs, hand-made by Native American artisans, at a craft store and through a Web site. Making these items requires a special type of clay and a large amount of individual person-hours, so for planning purposes, the company would like to forecast future demand, specifically through its Web site, which has increasingly become the primary source of sales. Following is the company’s Web site demand (in items sold) for the past 36 months: Develop a linear trend forecast model, an exponentially smoothed model (a = 0.20), and a 5-month moving average forecast model and indicate which one you think should be used to forecast Web site demand.
Find out the next month after april income by using the forecasting method of the moving average method
The following table shows a company's annual revenue (in billions of dollars) for 2009 to 2014. Year Period (t) Revenue 2009 1 23.8 2010 2 29.2 2011 3 37.8 2012 4 50.1 2013 5 59.9 2014 6 66.8 -Develop a linear trend equation for this time series to forecast revenue (in billions of dollars). (Round your numerical values to three decimal places.) -What is the average revenue increase per year (in billions of dollars) that this company has been realizing? (Round your answer to three decimal places.) -Compute an estimate of this company's revenue (in billions of dollars) for 2015. (Round your answer to two decimal places.)
The table below represents the monthly unemployment rates in the US from January of 2005 through May of 2016. Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2005 5.3% 5.4% 5.2% 5.2% 5.1% 5.0% 5.0% 4.9% 5.0% 5.0% 5.0% 4.9% 2006 4.7% 4.8% 4.7% 4.7% 4.6% 4.6% 4.7% 4.7% 4.5% 4.4% 4.5% 4.4% 2007 4.6% 4.5% 4.4% 4.5% 4.4% 4.6% 4.7% 4.6% 4.7% 4.7% 4.7% 5.0% 2008 5.0% 4.9% 5.1% 5.0% 5.4% 5.6% 5.8% 6.1% 6.1% 6.5% 6.8% 7.3% 2009 7.8% 8.3% 8.7% 9.0% 9.4% 9.5% 9.5% 9.6% 9.8% 10.0% 9.9% 9.9% 2010 9.7% 9.8% 9.9% 9.9% 9.6% 9.4% 9.5% 9.5% 9.5% 9.5% 9.8% 9.4% 2011 9.1% 9.0% 9.0% 9.1% 9.0% 9.1% 9.0% 9.0% 9.0% 8.8% 8.6% 8.5% 2012 8.2% 8.3% 8.2% 8.2% 8.2% 8.2% 8.2% 8.1% 7.8% 7.8% 7.8% 7.9% 2013 7.9% 7.7% 7.5% 7.5% 7.5% 7.5% 7.3% 7.2%…
1. Explain, why doesn’t an estimated absolute covariance number tell the investor much about the relationship between the returns on the two assets?
Which of the following time series forecasting methods would not be used to forecast seasonal data?
The sales records of a major auto manufacturer over the past years from 2005 to 2019 are shown below. Number of Cars Sold Year (In thousands of Units) 2005 195 2006 200 2007 250 2008 270 2009 320 2010 380 2011 440 2012 460 2013 500 2014 500 2015 550 2016 570 2017 600 2018 650 2019 700 Develop moving average with k=4 and compute its MSE Develop exponential model with α = 0.4 and compute its MSE Develop a linear trend model and compute its MSE Based on the MSE computed fore ach model , which model fits well to the data Based on the answer of question (4) forecast the number of cars sold for year 2020.

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