Uppgift 13: Consider the following time series data:Month 1 2 3 4567Value 24 13 20 12 19 23 15Use a smoothing constant a = 0.2 to compute the exponential smoothingforecasts for the time series. Compute MSE, and a forecast for week 8.Use a smoothing constant of a = 0.4 to compute the exponential smoothinga.b.forecasts.Does a smoothing constant of 0.2 or 0.4 appear to provide more accurateforecasts based on MSE? Explain.A complete answer should be given that is easy to follow step by step.C.

In order to forecast for the period t+1 using the values (actual value At and forecasted value Ft)…

Answered: Uppgift 13: Consider the following time…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

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
Please answer part e
1.8 Compute and compare the total electricity consumption of Sub-Saharan Africa to the United States in 2012. The population of Sub-Saharan Africa was 926 million, and the United States was 314 million. The per person consumption in the United States in 2012 was 12.96 MWh per year. Consult Fig. 1.11 for the per person consumption in Sub-Saharan Africa.
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.
Q. 14 Explain the Graphic method of trend estimation in a time series and give its merits and demerits.
Consider the following time series data. Month 1. 3 4 5 7 Value 20 11 19 11 19 22 15 Round your answers to two decimal places. a. Compute MSE using the most recent value as the forecast for the next period. Mean squared error is What is the forecast for month 8? b. Compute MSE using the average of all data available as the forecast for the next period. Mean squared error is What is the forecast for month 8? c. Which method appears to provide the better forecast? - Select your answer -
Consider the following quarterly time series. Quarter 1 2 3 4 Year 1 923 Year 2 1,112 1,056 1,156 992 Year 3 1,078 1,243 1,124 1,124 1,254 1,301 1,198 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple 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 based on the model developed in part b.
The figure below illustrates monthly data over 10 years. What method would you expect would perform the best at forecasting the data series in period 121? Explain the (i) strengths of your selected method for this data and the (ii) weaknesses of alternative methods for this data. Consider the concepts of level, seasonality, trend, and noise in your answer.
In Table L-1, the total value of imports of Indonesian goods and services from China for the 2010-2019 period is reported on a quarterly basis. With this data you are asked to analyze it with the Trend model and Seasonal Index. Question: a. Calculate a moving average with the average period length of 4 or MA (4) [why four?] And put the results in a column. Beware of selecting the rows to place the average result. b. Calculate the moving average with the length of the average period is 2 from the data in column MA (4) and the result is given the symbol MA (2x4) and the result is placed in a column. Beware of selecting the rows to place the average result. c. Calculate the ratio to the Moving Average (Y / MA (2x4)) and place it in a new column! d. Calculate a seasonal index and interpret the results! e. Create a Deseasonalized data series and place it in a new column! f. Plot the Dessasonalized data series and read the long-term movement patterns of data! Remember that understanding of…
Consider the time series data in the table below (a-c all count as one problem) a. Construct a time series plot. What type of pattern exists in the data?b. Develop a three-week moving average for this time series. Compute MSEand a forecast for week 8. c. Use α = 0.2 to compute the exponential smoothing value for the time series.Compute MSE and a forecast for week 8.
Refer to the gasoline sales time series data in the given table. Compute four-week and five-week moving averages for the time series. Round your answers to two decimal places. Compute the MSE for the four-week and five-week moving average forecasts. Round your intermediate calculations and final answers to two decimal places. What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Recall that MSE for the three-week moving average is 12.89.
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

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS