What is time series decomposition? Why would a forecaster choose this method versus some of the other methods we've learned in class? In your answer, please make sure to define the components of a time series.
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A: The correct answer is
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Q: Which of the following measures of forecast fit may correctly be used to compare different forecast…
A: Answer as follows:
Q: Determine the Running Sum of Forecast Errors (RSFE), the Mean Absolute Deviation, MADt-1,and the…
A: Forecasting is the process of predicting future demand based on previous or historic information and…
Q: After using your forecasting model for six months, you decide to test it using MAD and a tracking…
A: Here we use formulae: Formulas: Tracking Signal (TS) is presented by:TS = RSFEMAD RSFE = Running…
Q: Using your own words, describe the drawbacks of the moving average forecasting model and the…
A: Definitions Moving average: - A forecast which is made by taking the average or weighted average of…
Q: Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 a. Use α = 0.2 to…
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Q: The manager wants to forecast the month 6's sales using the following historical data: Months Month…
A: Forecasting is the process of estimating the future demand using the previous years information and…
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A: Naive forecasting is an forecast estimation technique in which the current period forecast is equal…
Q: Forecasts are usually classified by time horizon into three categories a. finance/accounting,…
A: Forecasts are usually classified by time horizon into three categories finance/accounting,…
Q: pros and cons of doing that? Give three examples of unethical conduct involving forecasting and the…
A: Unethical behavior takes place in forecasting when an analyst specifies a particular data to create…
Q: If a forecast can be made using a quantitative model, a forecaster need not use her personal opinion…
A: This do not require any introduction
Q: 16- Statistical forecasting models have the following weaknesses, except __________. a. Can be…
A: Forecasting refers to the process to predict the future values of a particular phenomenon. The…
Q: The following tabulations are actual sales of units for six months and a starting forecast in…
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Q: 1. A forecaster must decide on the value of this factor before he can use the simple moving average…
A: Since you have asked multiple questions, we will solve the first question for you. If you want any…
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A: Forecasting is a technique used to predict future outcomes on the basis of past data. In business…
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Q: Consider the total production (and sales) of ice cream in Canada (in millions of liters) for the…
A: Since we only answer up to 3 sub-parts, we will answer the first 3. Please resubmit the question and…
Q: A company wants to forecast demand using the weighted moving average. If the company uses two prior…
A: Weighted moving average (WMA) = Wt * Vt
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A: Forecasting can be defined as the process of estimating data based on the present and past data.…
Q: forecasting methods across different data sets?
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Q: what are the differences between the following models? 1) Moving average models 2) Simple…
A: As per guidelines, we would provide only three sub-parts at a time. Please provide each question at…
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A: Answer: Relief aid needed following an earthquake, belong to the category of disaster relied supply…
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A: Given data is
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A: Given data is
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Q: Forecasting is a prediction rather than a reality
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A: Forecasting is a technique used to predict future outcomes on the basis of past data. In businesses…
Q: The intuition behind the MAE metric to evaluate old forecasts is:a. to sum up the forecast errors.b.…
A: The intuition behind the MAE metric to evaluate old forecasts is: To sum up the forecast errors. To…
Q: How does the linear trend line forecasting model differ from a lincar regression model for…
A: Linear trend line forecasting refers to the statistical tool that helps in better interpretation of…
Q: a. Find the tracking signal for each month. (Negative values should be indicated by a minus sign.…
A: Based on the above provided question, the tracking signal for each month can be obtained as below:
Q: The moving average forecast method should only be used with time series data demonstrating a linear…
A: A moving average, which is indeed the average of any subset of values, is a method for gaining a…
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Q: Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14 16 12 17 15 A. Develop the…
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What is time series decomposition? Why would a
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- Under what conditions might a firm use multiple forecasting methods?The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
- The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
- A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b? Is it guaranteed to produce better forecasts for the future?
