is the series stationary or not? is there's a trend? 4035Quarterly GVA of banana industry3025202000Quarterly GVA of banana industry in the Phillippines from January 2000 to December 2021wwwлим2005Barn2010Yearmm2020ли2015

The trend is defined as the upward or downward movement of time series over a long period of time.…

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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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Question Annual sales (in million taka) by a chain shop are provided below.Year201020112012201320142015201620172018Sales6812181513161210(a) Estimate first order autocorrelation function ?(1) and use this to comment on the properties of the time series.(b) Obtain forecast for the year 2019 from the trend model ?̂?=5.48+4.52?−0.50?2.
Ten years of monthly data of a seasonally adjusted series are used to estimate a linear trend model as T = 24.10+ 0.32t. In addition, seasonal indices for January and February are calculated as 1.08 and 0.97, respectively. Make a forecast for the first two months of next year. (Do not round intermediate calculations. Round your answers to 2 decimal places.) ýt January February
Suppose the following are the seasonal indices for the first three quarters of the year for a quarterly series: Quarter Seasonal Index Q1 73.7 Q2 82.5 Q3 108.1 Remember that the seasonal indices should average 100 so you should be able to infer the seasonal index for Q4. Furthermore, suppose that the estimated coeffcients from a regression of the deseasonalized series on Time are given below: Coefficients Intercept 2,733 Time 65.8 If the original value of the series in a Q4 was 2,197, then what is the seasonally adjusted value?
Number of Certified Organic Farmsin the United States, 2001–2008 Year Farms 2001 5,153 2002 5,454 2003 6,178 2004 6,150 2005 6,598 2006 7,486 2007 9,248 2008 11,043 Fit three trends (linear, quadratic, exponential) to the time series. (A negative value should be indicated by a minus sign. Do not round the intermediate calculations. Round your final answers to 2 decimal places.) Linear yt = xt + Quadratic yt = xt2+ xt + Exponential yt = e x Use each of the three fitted trend equations to make numerical forecasts for the next three years. (Round the intermediate calculations to 2 decimal places and round your final answers to 1 decimal place.) t Linear Exponential Quadratic 9 10 11
The following table shows the daily receipts in millions of dollars of the movie "Avatar" for successive Fridays after its opening on Friday 18 December 2009.Estimate the instantaneous rate of change of daily receipts 20 weeks after the opening day. Round to four decimal places.
Question 2: Find the trend for the time series using moving average method. Forecast for Year 4 quarter 4 Year Sales (000's) Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 25 42 55 36 16 50 53 40 28 58 69 59 2N 3.
The trend equation for a time series is fitted as Y. Shift the trend origin from 1998 to 1993, given that the time unit is 1 year. = 60 + 1.5X
When a time series is stationary, the appropriate forecasting technique(s) is (are). Multiple Cholce O simple exponential smoothing only simple moving averages or simple exponential smoothing simple moving averages only regression analysis or seasonal indexing
The following time-series diagram tracks the performance of two stocks during the month of October. Describe the overall trend of the two stocks. By what percentage did the value of each stock increase or decrease during the month of October? Stock 2 $200 Stock 1 T$150 Stock 2 -$100 Stock 1 T$ 50 1 5 11 16 21 26 31 October a. Stock 2 increased by 200%. Stock 1 decreased by 50%. b. Stock 2 increased by 100%. Stock 1 decreased by 50% c. Stock 2 increased by 200%. Stock 1 decreased by 33% d. Stock 2 increased by 100%. Stock 1 decreased by 33%.
Average temperature in Winter and Summer in a region for 2016-2019 is given below. Year Winter Summer 2016 20 45 2017 35 44 2018 28 43 2019 29 48 Assume the additive time series model with no trend and using the above data, answer the following questions. 1. Compute two period moving averages MA(2). 2. What is the advantage of computing MA(2). 3. Do you need to compute CMA. Why or why not. 4. Estimate the seasonal factors for Winter and summer. 5. Forecast the average temperature for Fall and Spring 2020 assuming CL=2 and IR=0
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

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