Forecasting And Predictive Analytics 7th Edition J. Holton Wilson, Barry Keating - Solutions

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3.4 Consider the quarterly electricity production for years 1{4:Year 1 2 3 4 Q1 99 120 139 160 Q2 88 108 127 148 Q3 93 111 131 150 Q4 111 130 152 170
(b) Write the whole smoothing operation as a single weighted moving average by ¯nding the appropriate weights.
(a) Explain the choice of the smoother lengths in about two sentences.
3.2 Show that a 3 £ 5 MA is equivalent to a 7-term weighted moving average with weights of 0.067, 0.133, 0.200, 0.200, 0.200, 0.133, and 0.067.3.3 For quarterly data, an early step in seasonal adjustment often involves applying a moving average smoother of length 4 followed by a moving average of
Period Shipments Period Shipments 1 42 9 180 2 69 10 204 3 100 11 228 4 115 12 247 5 132 13 291 6 141 14 337 7 154 15 391 8 171
3.1 The following values represent a cubic trend pattern mixed with some randomness. Apply a single 3-period moving average, a single 5-period moving average, a single 7-period moving average, a double 3£3 moving average, and a double 5 £ 5 moving average. Which type of moving average seems most
the seasonal component can be forced to be constant over time(i.e., the same seasonal component for each year);holiday e®ects ² holiday factors (such as Easter, Labor Day, and Thanksgiving)be estimated.automatic ARIMA model selection is available.
Other changes in the level of the series such as level shifts and temporary ramp e®ects can also be modeled.missing values in the series can be estimated and replaced;
Outliers arise because of unusual circumstances such as major strikes. These e®ects can also be removed prior to decomposi-tion.
A seasonal sub-series plot for the decomposition shown in Figure 3-11. The seasonal component for June, July, and August became larger over the period of the data, with corresponding falls in February and March.
(g) The world motor vehicle market was greatly a®ected by the oil crisis in 1973{1974. How did it a®ect Japanese motor vehicle production? If this information could be included in the forecasts, how would it a®ect the values of the MSE and MAPE?
(f) From the graphs you have made, can you suggest a better forecasting method?
(e) Transform your forecast for 1990 back to the original scale by ¯nd the exponential of your forecast in (c). Add the forecast to your graph.
(d) Compute the forecast errors and calculate the MSE and MAPE from these errors.
(c) Calculate forecasts for the transformed data for each year from 1948 to 1990 using NaÄ³ve Forecast 1.
(b) Transform the data using logarithms and do another time plot.
(a) Plot the data in a time plot. What features of the data indicate a transformation may be appropriate?
2.8 Japanese motor vehicle production for 1947{1989 is given in Table 2-22.
(e) Show that the graphed forecasts are identical to extend-ing the line drawn between the ¯rst and last observations.
(d) Add the forecasts to the graph.
(c) From these forecasts, compute forecasts for the original index for each of the 20 days.
(b) Forecast the change in the index for each of the next 20 days by taking the average of the historical changes.
(a) Calculate the change in the index for each day by sub-tracting the value for the previous day. (This is known as \di®erencing" the data and is discussed in Chapter 6.)
2.7 Download the Dow Jones index from the web page and pro-duce a time plot of the series using a computer package.
(c) Repeat Part (b) using columns 1 and 3 below. Which forecasting method appears to be better?
(b) For each method, compute the Mean Error, Mean Abso-lute Error, Mean Squared Error, Mean Percentage Error, and Mean Absolute Percentage Error using equations(2.13) through (2.18).
(a) Plot the actual demand on a graph along with the fore-casts from the two methods.
2.6 Column 1 on the following page is the actual demand for prod-uct E15 over 20 months. Columns 2 and 3 are the one-month ahead forecasts according to two di®erent forecasting models to be discussed in Chapter 4. (Method 1 gives forecasts from Table 4-4 and Method 2 gives forecasts from Table
(d) Why is it inappropriate to calculate the autocorrelation of these data?
(c) Calculate the correlation of the two variables and pro-duce a scatterplot of Y against X.
(b) Which of these statistics give a measure of the center of data and which give a measure of the spread of data?
(a) Calculate the mean, median, MAD, MSD, and standard deviation for each variable.
2.5 Table 2-21 shows data on the performance of 14 trained female distance runners. The variables measured are the running time (minutes) in a 10 kilometer road race and the maximal aerobic power collected during the week following the run.
2.4 In the graphs on the previous page, four time series are plotted along with their ACFs. Which ACF goes with which time series?
(e) Quarterly production of bricks (in millions of units) at Portland, Australia (March 1956{September 1994).
(d) Monthly total of accidental deaths in the United States(January 1973{December 1978).
(c) Number of lynx trapped annually in the McKenzie River district of northwest Canada (1821{1934).
(b) Daily morning temperature of a cow for 75 days.
(a) Monthly total of people on unemployed bene¯ts in Aus-tralia (January 1956{July 1992).
2.3 For each of the following series on the web page, make a graph of the data (using a computer package), describe the main features and, if transforming seems appropriate, do so and describe the e®ect.
(d) Weekly electricity consumption for your local area over the past 10 years.\
(c) Daily sales at a fast-food store for six months.
(b) Hourly pulse rate of a person for one week.
(a) Monthly retail sales of computer disks for the past 10 years at your local store.
For each of the following series, what sort of time patterns would you expect to see?
(b) Make a time plot of the data. Is there any time pattern in the temperature readings?
(a) What is your best estimate of the average temperature in June 1995?
2.1 Table 2-20 gives average monthly temperatures in Paris.
Monthly milk production per cow over 14 years (Source: Cryer, 1986). The second graph shows the data adjusted for the length of month. This yields a simpler pattern enabling better forecasts and easier identi¯cation of unusual observations.
Transformations of the electricity production data. Of these, either a square root, cube root, or a log transformation could be used to stabilize the variation to form a series which has variation approximately constant over the series.
The electricity production data shown in Figure 1-2a (p. 7)exhibit a strong trend in addition to the monthly seasonality.The beer data in Figure 2-1 show no trend.Many data series include combinations of the preceding patterns.For example, Figure 1-2d (p. 7) shows trend, seasonality, and cyclical
4. A trend (T) pattern exists when there is a long-term increase or trend decrease in the data. The sales of many companies, the gross national product (GNP), and many other business or economic indicators follow a trend pattern in their movement over time.
3. A cyclical (C) pattern exists when the data exhibit rises and cyclical falls that are not of a ¯xed period. For economic series, these are usually due to economic °uctuations such as those asso-ciated with the business cycle. The sales of products such as automobiles, steel, and major
2. A seasonal (S) pattern exists when a series is in°uenced by seasonal seasonal factors (e.g., the quarter of the year, the month, or day of the week). Sales of products such as soft drinks, ice creams, and household electricity consumption all exhibit this type of pattern. The beer data show
1. A horizontal (H) pattern exists when the data values °uctuate horizontal around a constant mean. (Such a series is called \stationary"in its mean.) A product whose sales do not increase or decrease stationary over time would be of this type. Similarly, a quality control sit-uation involving
In the chapter example, we used a very small sample taken from two Usenet newsgroups (autos and electronics). Use the much larger data set that includes many postings from each of the two groups and again use XLMiner© to attempt to classify the groups as either “auto”-related or
Trivago is a German technology company that is essentially a hotel price comparison website. It claims to be the world’s largest online hotel search site. As part of the information on their websiteTrivago displays the Trivago Rating Index (TRI), a number between 0 and 100 for every hotel (with
A twenty-foot equivalent unit (TEU) is a standard measurement of volume in container shipping. The majority of containers are either 20 or 40 feet in length. A 20-foot container is 1 TEU; a 40-foot container is 2 TEUs. Although the height of containers can also vary, this does not affect the TEU
Wine (in this case red wine) has been graded for many years by experts who actually taste a sample of the wine, examine its color and aroma, and assign a grade (in our case, high quality or lower quality). Would it be possible, however, to use attributes of the wine that are machine measurable like
The Bank Marketing Data includes actual information for a direct marketing effort by a bank. We will attempt to construct a model with just a few of the available attributes. We are interested in classifying whether a customer will respond positively to the marketing effort offering a term
The Boston Housing data includes information from the 1970 Census for the city of Boston and surrounding area. Note that there are two variables representing value; one is “Medv” which is a dollar value. The other is “Cat Medv” which is a binary variable indicating whether the house is of
The Credit Card Fraud data is a small version (comprised of 12,240 records) of a much larger data set (containing 248,807 records); it is made up of 2013 European transactions. It is a very unbalanced data set in which there are only a few fraudulent transactions. Attempting to classify
Explain how the predictions made using analytics are somewhat different than those from traditional forecasting models. Are the data used different? Are the types of predictions different?
Suppose that you have been asked to recommend a forecasting technique that would be appropriate to prepare a forecast, given the following situational characteristics:a. You have 10 years of quarterly data.b. There is an upward trend to the data.c. There is a significant increase in sales prior to
What are the two main things to consider when selecting a forecast method? Why?
Describe the nine-step forecast process presented in the chapter.
What two groups must communicate well in order for the forecast process to be effective? Explain why.
Explain the process of going from raw data to actions based on a forecast.
Discuss how forecasting has moved from purely judgmental methods to highly complex methods.
Why mine text at all? Isn’t language so complex that little useful insight can be gained through machine learning methods?
In previous chapters, we used data mining diagnostic statistics such as confusion matrices and lift charts to evaluate models. Are these types of statistics useful in text mining?
In the chapter, the two different text mining approaches were both used to mine text: “bag of words” analysis and “natural language processing.” What is the significant difference between the two approaches? Which approach preceded the other historically?
What is meant by “natural language processing"?
What is meant by “bag of words” analysis?
What is meant by the term dimension reduction?
What method explained in this chapter is a concept in analytics of approximating the sampling distribution of a statistic by repeatedly sampling from a given sample of size n.
“Boosting” may only be used with Naïve Bayes algorithms. Is this a true statement?
“Bagging” is a short form of “bootstrap aggregation.” Explain conceptually how bagging is accomplished in software. Why might bagging increase the predictive capacity of an underlying algorithm?
What are “weak learners” and are they used by bagging or boosting? Explain the concept of a weak learner.
Explain the fundamental difference between “boosting” and “bagging.”
What is the basic concept underlying an “ensemble” model?
Explain what is meant by Bayes’ theorem as used in the Naive Bayes model. Lift Chart (Validation Dataset) Decile-wise Lift Chart (Validation Dataset) 10- 250 200- 150 100- 2- 50 of 2000 12 3 4 5678 9 10 tutut 1000 3000 cases Deciles Cumulative personal loan when sorted using predicted values
Calculate the classification error rate for the following confusion matrix. Comment on the pattern of misclassifications. How much better did this data mining technique do as compared to a naive model? What is the misclassification rate for the naïve model?
A data mining algorithm has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) and 952 as nonfraudulent (920 correctly so). Which of the following situations represents the confusion matrix for the transactions data mentioned? Explain your reasoning.
The lift chart and the confusion matrix are both standard diagnostic tools used to evaluate a data mining algorithm. Don’t the two measures show-display the same information? Explain any differences between the two measures.
Data has the characteristic of “non-rivalry.” What is non-rivalry and why is it important to realize that data has this characteristic?
In the Universal Bank data in this chapter only 10% of the records represented customers that had taken out a personal loan (the target variable). If we were to score a new customer based upon the attributes we used in the algorithm, we would be accurate in the prediction about 90% of the time if
Show the computation for the misclassification rate of this confusion matrix.Confusion MatrixActual\Predicted01097020128
The validation data set confusion matrix for the Universal Bank data classification model is shown.How many records were in the validation data set? How many of the records were correctly classified by the algorithm? How many records were incorrectly classified? What is the “misclassification $Validation: Classification Summary Confusion Matrix Actual\Predicted 1809 80 3 108$
Some data mining algorithms work so “well” that they have a tendency to overfit the training data. What does the term “overfit” mean and what difficulties does overlooking it cause for the data scientist?
In the Universal Bank classification model estimated with XLMiner the software produced the validation data set lift chart shown.How is the naïve model displayed in the diagram? What does the other line in the model represent? Lift Chart (Original) Cumulative actual when sorted using predicted
How do “structured” and “unstructured” data differ? Which is the more prevalent form of data?
The first step in data mining procedures according to SAS and IBM/SPSS is to “sample” the data. Sampling here refers to dividing the data available for analysis into at least two parts: a training data set and a validation data set. Why do both SAS and IBM/SPSS recommend this as a first step?
A classification model's misclassification rate on the validation set is a better measure of the model's predictive ability on new (unseen) data than its misclassification rate on the training set. Explain whether this statement is accurate and why that is so.
The data below show retail sales at hardware stores in the United States monthly between January 1992 and December 2005. The data are in millions of dollars and are not seasonally
The following table contains quarterly data on Upper Midwest car sales (CS) in thousands for 1996 Q1 through 2016 Q4: Upper Midwest Car Sales
A regional supplier of jet fuel is interested in forecasting its sales. These sales data are shown for the period from 2002Q1 to 2017Q4 (data in billions of gallons): Jet Fuel Sales (Billions of
The Bechtal Tire Company (BTC) is a supplier of automotive tires for U.S. car companies. BTC has hired you to analyze its sales. For this problem, do all the work in Forecast X™ and be sure to request the MAPE in the statistics tab. Data from 1995 Q1 through 2016 Q4 are given in the following
Carl Lipke is the marketing VP for a propane gas distributor. He would like to have a forecast of sales on a quarterly basis, and he has asked you to prepare a time-series decomposition model. The data for 2005 through 2016 follow:Propane Gas Sales in Millions of Pounds (Total at End-Month of Each
A tanning parlor located in a major shopping center near a large New England city has the following history of customers over the last four years (data are in hundreds of customers and months are the midmonth of each quarter): YearFebMayAugNovYearly

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Forecasting And Predictive Analytics 7th Edition J. Holton Wilson, Barry Keating - Solutions

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