The objective of this research is to develop a model to forecast sales for an ice-cream company. In order to achieve this objective, we evaluate sales data of three ice-cream flavors namely vanilla, chocolate, and Tally Ho (mixture of chocolate and vanilla) from January 2016 to 25 November 2019. To determine which model worked the best, we tested different models such as moving averages, simple exponential smoothing, Holt's method, Winters' method, method modeling seasonality and trend, and an ensemble method. We found Winters' method and modeling seasonality and trend performed well in terms of lowest error rates compared with other methods.
Predicting a patient's length of stay (LOS) in a hospital setting has been widely researched. Accurately predicting an individual's LOS can have a significant impact on a healthcare provider's ability to care for individuals by allowing them to properly prepare and manage resources. A hospital's productivity requires a delicate balance of maintaining enough staffing and resources without being overly equipped or wasteful. This has become even more important in light of the current COVID-19 pandemic, during which emergency departments around the globe have been inundated with patients and are struggling to manage their resources.In this study, the authors focus on the prediction of LOS at the time of admission in emergency departments at Rhode Island hospitals through discharge data obtained from the Rhode Island Department of Health over the time period of 2012 and 2013. This work also explores the distribution of discharge dispositions in an effort to better characterize the resources patients require upon leaving the emergency department.
We study the performances of various predictive models including decision trees, random forests, neural networks, and linear discriminant analysis on an imbalanced data set of home loan applications. During the process, we propose our undersampling algorithm to cope with the issues created by the imbalance of the data. Our technique is shown to work competitively against popular resampling techniques such as random oversampling, undersampling, synthetic minority oversampling technique (SMOTE), and random oversampling examples (ROSE). We also investigate the relation between the true positive rate, true negative rate, and the imbalance of the data.
The purpose of this study is to determine which quantitative metrics are most representative of investor sentiment in the US equity markets. Sentiment is the aggregation of consumers', investors', and producers' thoughts and opinions about the future of the financial markets. By analyzing the change in popular economic indicators, financial market statistics, and sentiment reports, we can gain information on investor reactions. Furthermore, we will use machine learning techniques to develop predictive models that will attempt to forecast whether the stock market will go up or down based on the percent change in these indicators.
The following chapter aims to investigate the impact on the prices of inferior goods from COVID-19, a global pandemic with a spread never before seen, and in order to stipulate the possible consequences, a contrast was made with past world crises in order to speculate the state in which the country and its citizens would be economically benefited. The chapter has been based on articles previously carried out by specialists, said essays, likewise, that have been contrasted by different authors; therefore, it could be stipulated that the methodology used throughout the trial was the one applied, since it was based on prior knowledge from great scholars on the subject. The results found indicate that, even though the country is in an uncertain outlook, the possibilities of economic recovery are high since previous economic crises denote large drops in money fluctuation with a high rate of recovery.
This chapter concerns itself with the development of a regression model for an executive compensation forecasting of the top-level executives of MetLife. The data observations consist of a list of 12 comparable corporations selected from comparable financial institutions. A set of 28 financial variables from each of the corporations is compiled as the data source of this regression model.
Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using supermarket data across two product categories, this chapter shows that using a bevy of forecasting methods improves forecasting accuracy. Accuracy is measured by the mean absolute percentage error. The optimal methods for one consumer goods product may be different than for another. The best model varied from sophisticated, most such as autoregressive integrated moving average (ARIMA) and Holt–Winters to a random walk model. Forecasters must be proficient in multiple statistical techniques since the best technique varies within a categories, variety, and product size.
A majority of products for manufacturing or consumers have multiple characteristics that must meet the requirements of the customer. For example, a steel beam any have dimensional tolerances on its length, width, or height and functional tolerances on its strength. The characteristics are influenced by different processes that create the product. For an individual characteristic, process capability measures exist that convey the degree to which the characteristic meets the specification requirements. Such measures may indicate the proportion of nonconforming product related to the particular characteristic, under some distributional assumptions of the characteristic. For products with multiple characteristics, the unit costs of rectification may be different, making the satisfaction of some characteristics meeting customer requirements more important than others. In this paper, an aggregate process capability performance measure is developed that considers the relative importance of the characteristic based on unit costs of nonconformance. Based on the aggregate measure, appropriate process capability measures for the individual measures are also derived. Bounds on the aggregate capability measures are also established.
In a previous chapter (Klimberg, Ratick, & Smith, 2018), we introduced a novel approach in which cluster centroids were used as input data for the predictor variables of a multiple linear regression (MLR) used to forecast fleet maintenance costs. We applied this approach to a real data set and significantly improved the predictive accuracy of the MLR model. In this chapter, we develop a methodology for adjusting moving average forecasts of the future values of fleet service occurrences by interpolating those forecast values using their relative distances from cluster centroids. We illustrate and evaluate the efficacy of this approach with our previously used data set on fleet maintenance.
The tension between the United States and China has not ended, despite the lengthy negotiations carried out in 2019 to appease differences and decrease protectionism over the technology industry, since the trade war was producing devastating consequences for both powers and international trade. The resurgence of tension is due to two main factors, such as mutual accusations about who is to blame for the origin of COVID-19 and the use of the new 5G mobile technology by Chinese companies like ZTE and Huawei. We developed an in-depth analysis of the consequences on international prices of the technology industry and international trade due to the reappearance of the trade conflict between China and the United States. This analysis will be carried out through the application of economic theories. There is greater distrust in the technology sector, and this produces a variation in the price of the technology industry. Likewise, according to Marshall's law of demand and supply, this effect can be explained more accurately; since there was a lower demand for technological products, the digital industry will have to lower its prices to generate income.
In China, more than 80,000 people have been diagnosed with COVID-19, and more than 3,000 people have lost their lives. It seems that there will be more deaths since the epidemic is not over. All the Chinese provinces have reported the COVID-19 cases. This chapter aims to explore the trend of COVID-19 treatment efficiency in Chinese provinces using the data released daily by China Center for Disease Control and Prevention. Since China Center for Disease Control and Prevention began to release data daily from January 24 to March 12, we have more than 40 groups of daily data for 31 provinces in China mainland. In the calculation, we take the daily data of each province as a sample and then we have more than 1,200 samples in this study.We use additive two-stage data envelopment analysis as an efficiency evaluation tool to calculate the COVID-19 treatment efficiency. In our framework, the first stage is to understand the infection rate and the second stage is to evaluate the treatment efficiency. In the first stage for the tth day, we use total population (p) and number of people infected in the previous day (inf t−1) as the inputs and cumulative number of people infected in the current day (inf t ) as the output. In the second stage for the tth day, we use cumulative number of people infected in the current day (inf t ) as the input and cumulative death in the current day (death t ) and cumulative recovery in the current day (recov t ) as the outputs. Some techniques on how to deal with undesirable outputs such as inf t and death t are employed in this study.After we have the infection rate and treatment efficiency for the samples more than 1,200, we analyze the COVID-19 treatment efficiency and its development trend from January 24 to March 12 in 34 regions of China from static and dynamic aspects. The results show that, on the whole, the overall efficiency and phased efficiency of COVID-19 treatment efficiency in all regions of China are relatively high, which reflects the key factor for the Chinese government to quickly control the epidemic in the short term. Relatively speaking, the average efficiency value in the infection stage (first stage) is lower than the average efficiency value in the healing stage (second stage), which shows that the focus of anti-epidemic in China should be early detection and prevention rather than treatment process. In terms of trend, the total efficiency of COVID-19 treatment in each region shows a trend of "increasing first and then decreasing." Our analysis indicates that in the initial stage, the continuous increase of various resources leads to the rise of the total efficiency, while in the later stage, the rapid decline of the number of infected people leads to the decrease of the total efficiency. Based on the results of the efficiency analysis, this study provides corresponding management implications and policy suggestions, hoping to provide some enlightenment and suggestions for the anti-epidemic work of other countries in the severe environment where the epidemic is spreading rapidly.
In this chapter, we consider the model of call center incoming call forecasting and staffing-level optimization. We first present the structure of the model and how an agent-based modeling technique could enrich the decision rule and the model. A matrix layout is introduced to present the model so that it can be understood in an efficient way from the perspective of a programmer. The agent-based queuing model will be used in forecasting. We then utilize the bisection method and stepwise method to optimize the staff level to satisfy a target range service-level criteria. Call center management could use the model in practice for their management forecasting and optimization decision-making process in terms of how many agents they need to achieve the target business efficiency goal.
The service industry is a major component of the economy. Raw material, components, assemblies, and finished products are shipped between suppliers, manufacturers, distributors, and retailers. Accordingly, timely receipt of shipped goods is crucial in maintaining the efficiency and effectiveness of such service processes. A service provider offers an incentive to the customer by specifying a competitive target time for delivery of goods. Further, if the delivery time is deviant from the target value, the provider offers to reimburse the customer for an amount that is proportional to the value of the goods and the degree of deviation from the target value. The service provider may set the price to be charged as a function of product value. This price is in addition to the operational costs of logistics that are not considered in the formulated model. For protection against deviation from target due dates, the service provider agrees to reimburse the customer. The reimbursement could be based on an asymmetric loss function influenced by the degree of deviation from the target due date as well as product value. The penalties could be different for early and late deliveries since the customer may experience different impact and consequences accordingly. The chapter develops a model to determine the amount (price) that the provider should add to the cost estimate of the delivery contract for protection against delivery deviations. Such a cost estimate will include the operational costs (fixed and variable) of the shipment, to which an amount is added to cover the expected payout to customers when the delivery time deviates from the target value. The optimal price should be such that the expected revenue will at least exceed the expected payout.
In recent years, the problem of classification with imbalanced data has been growing in popularity in the data-mining and machine-learning communities due to the emergence of an abundance of imbalanced data in many fields. In this chapter, we compare the performance of six classification methods on an imbalanced dataset under the influence of four resampling techniques. These classification methods are the random forest, the support vector machine, logistic regression, k-nearest neighbor (KNN), the decision tree, and AdaBoost. Our study has shown that all of the classification methods have difficulty when working with the imbalanced data, with the KNN performing the worst, detecting only 27.4% of the minority class. However, with the help of resampling techniques, all of the classification methods experience improvement on overall performances. In particular, the Random Forest, in combination with the random over-sampling technique, performs the best, achieving 82.8% balanced accuracy (the average of the true-positive rate and true-negative rate). Keywords Imbalanced data Resampling Under-sampling Classification Random Forest Rare event detection Imbalanced learning Citation Nguyen, S., Niu, G., Quinn, J., Olinsky, A., Ormsbee, J., Smith, R.M. and Bishop, J. (2019), "Detecting Non-injured Passengers and Drivers in Car Accidents: A New Under-resampling Method for Imbalanced Classification", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 13), Emerald Publishing Limited, Bingley, pp. 93-105. https://doi.org/10.1108/S1477-407020190000013011 Publisher: Emerald Publishing Limited Copyright © 2019 Emerald Publishing Limited Introduction The emergence of Big Data, where data grow at an increasing rate in volume, velocity, and variety (Zikopoulos, Eaton, deRoos, Deutsch, & Lapis, 2011), in many disciplines in the past decades, poses many challenges for the fields of statistics, machine learning, and data mining. One of these significant issues is the problem of classification with imbalanced data, where the values for the dependent variable (target variable, class variable) are distributed highly unevenly. For example, these values (observations) might occur less than 1% of the time. In the past years, this topic has drawn a large amount of attention in machine learning, statistics, and other data-related communities (Krawczyk, 2016). In imbalanced data classification, the minority class (positive class) is the class preferred to be correctly classified and usually costs more when misclassified, that is, a false negative is more severe than a false positive. For instance, in cancer detection, wrongly diagnosing a cancer patient may result in the loss of life. The main trouble with classifying imbalanced data using traditional classification algorithms is that the true-positive rate, or sensitivity, is often very low. This is due to the fact that most of these traditional algorithms assume the values for the target variable is distributed equally. Thus, when applied to imbalanced data, the algorithms tend to favor the majority class, resulting in a high true-negative rate and low true-positive rate. To improve the true-positive rate while maintaining a good overall performance in imbalanced classification, a number of techniques have been developed. These techniques mainly fall into three categories: resampling method, modifying the traditional algorithm, and a combination of these two. While the goal of resampling is to balance the data, algorithmic methods reconstruct the objective functions in classification methods in order to give higher weights for the minority class. Some of the most popular resampling techniques include the following: synthetic minority over-sampling technique (SMOTE) (Chawla, Bowyer, Hall, & Kegelmeyer, 2002), random over-sampling examples (ROSE) (Menardi & Torelli, 2014), and borderline-SMOTE (Han, Wang, & Mao, 2005). Examples of classification methods that have been modified to work with imbalanced datasets include the following: Random Forest (Chen, Liaw, & Breiman, 2004) and the decision tree (Cieslak & Chawla, 2008) In this chapter, we compare the performances of six popular classification methods (the decision tree, the Random Forest, logistic regression, SVM, AdaBoost, and k-nearest neighbor (KNN)) coupled with four resampling techniques (random under-resampling, random over-resampling, SMOTE, and ROSE) on a car accident dataset. We then propose an under-resampling technique that can further improve the performance of the best model out of the six – the Random Forest. Data Our data are obtained from the accident dataset from the fatality analysis reporting system (FARS) and pertains to 2015. The dataset has 80,565 observations, where each observation represents a person involved in the car accident, and 25 variables, containing information such as the person’s sex, age, the location of the accident, and the time and date it took place. Our target variable contains the aftermath injury information. All observations are grouped into two classes: “Injured” class, including people who were injured in the accident, and “Not Injured” class. Thus, the target variable either takes the value of “Injured” or “Not Injured.” The “Not Injured” class accounts for 24.4%, which can be considered as the minority class as well as the positive class. We provide several graphs below representing the relation between the target variable and a few other input variables. From the graph shown in Fig. 1, we can see that the proportion of injured people in the group of people who drank alcohol before the accidents is greater than that of in the group of people who did not drink. Also, Fig. 2 suggests that the variable sex and the target variable seem to be independent of each other as given the information about sex, the proportion of injured people does not change much.Opens in a new window.Fig. 1. Distribution of the Target Variable in Different Categories of Drinking. Opens in a new window.Fig. 2. Distribution of the Target Variable with Respect to Gender. We also observe from Fig. 3 that the distribution of age in both classes is quite similar, with the majority of people being young (less than 30). Fig. 4 indicates that there seems to be more injuries in accidents occurring from midnight to noon than at other times.Opens in a new window.Fig. 3. Approximate Density Distribution of People’s Age Who Were in Fatal Car Accidents. Opens in a new window.Fig. 4. Approximated Density Distribution of the Hour When Accidents Occurred. Method In this section, we describe the classification methods and resampling methods used in our computation. We also introduce our proposed method to further improve the Random Forest, which is the “best” model (the model that gives the greatest averaged balanced accuracy) out of the six classification models. To test the performance of a classification model, we partition the original data into two datasets: training dataset (70%) and testing dataset (30%). The model is then built using the training dataset and once it is built, it is run and its predictions are checked on the testing dataset. There are missing values in the data, which we impute by the predictive mean-matching method (Rubin, 1986). All the calculations are done in R. The six classification methods we use are the decision tree, the Random Forest, logistic regression, KNN, the SVM, and AdaBoost. Decision Tree The decision tree or classification and regression tree (CART) (Breiman, Friedman, Olshen, & Stone, 1983) is a machine-learning algorithm that can be used with both a categorical target (classification tree) and a continuous target (regression tree). The decision tree structure is a flow chart where at each of its nodes, the incoming data are partitioned into multiple subsets based on a splitting rule. The final nodes, where the splitting stops, are called the leaves and are used for prediction. The tree is usually fully grown to obtain the most complicated tree and then pruned down to a simpler tree to avoid the over-fitting issue. In this calculation, we use a binary tree (in which a node can only have two branches) with the splitting rule using the Gini Index. We use package “rpart” (Recursive PARTition) for the computation. Random Forest The Random Forest is an ensemble method, which is the method that combines the results of multiple other methods. In the Random Forest, a set of decision trees is built on a subset of the original data. Each tree does not use all the variables but only a randomly selected subset of them to decide the split points. The Random Forest, then, makes predictions by majority voting between the trees. For instance, if three out of five trees of a Random Forest predict “Not Injured,” then the Random Forest will also predict “Not Injured.” Two main hyperparameters (unknown quantities that are determined by users) of the Random Forest are the number of trees and the number of variables selected at each node of a tree. In our calculations, we set the number of trees to 30 and the number of variables at each node to 2. Our computations of the Random Forest are carried out using the library “ranger” in R. Logistic Regression Logistic regression is one of the older and well-understood classification methods developed by David Cox in 1958 (Cox, 1958). Logistic regression is a linear classification method, that is, it forms a hyperplane that separates the negative and positive observations. The equation connects p , the probability of the target variable Y being positive, and the input variable vector, X , as follows. logit ( p ) = log p 1 − p = a + b X This equation can be written in the form of a logistic function, the inverse of the logit function. To find the parameters (the slope and the intercept), maximum-likelihood methods are often used. In our study, we use the package “glm” (generalized linear models) to compute logistic regression. K-Nearest Neighbor (KNN) KNN is one of the simplest supervised machine-learning algorithms. For classification, the prediction of a new observation is the majority of its KNN s. For example, if three out of five of the closest points of a new observation are classified as positive, then the 5-nearest neighbor will predict the new observation also as positive. To measure the “nearest,” a variety of distance measures can be applied, with the most popular one being the Euclidean distance. The hyperparameter for KNN is k , the number of nearest neighbors. We set k = 5 and use function KNN in the library class in R for our computation. Support Vector Machine (SVM) SVM was proposed by Vapnik in 1963 for the linear case (Vapnik & Lerner, 1963). The non-linear kernel version of SVM and the soft-margin SVM were introduced in 1992 (Boser, Guyon, & Vapnik, 1992) and in 1995 (Cortes & Vapnik, 1995). If the data are linearly separable, that is, there exists a hyperplane that separates the positive and negative classes, SVM looks for a hyperplane that separates the two classes with a maximum margin, where the margin is the distance of the closest point to the hyperplane. If the data are non-linearly separable, SVM can “soften” the margin to allow data points to cross the margin. The number of errors caused by data points positioned in a wrong region is controlled by the cost parameter. One can also use the “kernel trick” to transform the data to be linearly separable. We implement SVM using the “SVM” function in the “e1071” library with the radial basic function kernel and the cost parameter set to 1. AdaBoost AdaBoost, or Adaptive Boosting, is designed to improve the performance of a “weak” classification algorithm. The key idea of AdaBoost is to pay more attention to misclassified observations by giving them higher weights than correctly classified observations. Based on this idea, AdaBoost generates a sequence of classification methods where each method in the sequence is focused more on learning the error of its previous classification. All of the classification methods in the sequence, then, contribute to the final prediction of AdaBoost. In our computation, we use AdaBoost to boost a decision tree. We set the number of iterations to 100, that is, the classification sequence contains 100 decision trees. Resampling Method One of the main reasons for resampling the imbalanced data is to balance it so that regular classification methods can work with these data. Here, we consider four of the most frequently used resampling methods: randomly under-resampling (RUS), randomly over-resampling (ROS), SMOTE, and ROSE. Resampling methods fall into two categories: under-sampling and over-sampling. While under-sampling aims to reduce the size of the majority/negative class down to that of the minority class, over-sampling methods add observations to the minority/positive class. In the four methods, only RUS is an under-resampling method, and the other three belong to the class of over-resampling methods. Due to their simplicity in concept and computation, RUS and ROS are very frequently used resampling methods. In RUS, the number of negative examples is removed randomly to balance the negative class with the positive class. In ROS, positive examples are added by bootstrapping with replacement on the original positive class. While the downside of RUS is removing the original data, which may contain important information to build a prediction at the later stage, the downside of ROS is increasing the computation intensity, which could be severe if the sample size is extremely large. SMOTE (Chawla et al., 2002) and ROSE (Menardi & Torelli, 2014) artificially generated new positive examples. In SMOTE, new examples are created from a point, for example, point A and its neighbors. New points are random points lying on the lines connecting A with its neighbors. In ROSE, the generation of new examples from the minority class corresponds to the generation of data from the kernel conditional density estimate of the minority class. In all four resampling techniques, we balance the data so that each of the classes accounts for approximately 50%. In SMOTE, we choose the number of KNNs to be 5. Selective Under-resampling (SUR) In AdaBoost, the correctly classified observations are considered “easier” observations to classify and are given smaller weights in the next iteration. Inspired by this idea, we propose an under-sampling procedure that eliminates “easier” negative observations before applying the traditional under-sampling method. The “easiness” of a negative example is measured by how close its predicted probability is to 0. By this measurement, a negative observation with predicted probability 0 would be the “easiest” negative example, whereas a negative example with predicted probability 1 would be considered the “hardest” negative example. Note also that all the negative examples with predicted probability greater than 0.5 are misclassified observations. To compute the predicted probability for the negative observations, first we run the Random Forest on the data. Here, we choose the Random Forest to implement this approach because it gives the best performance out of the six tested methods. Our under-resampling and modeling works as follows: Step 1: run the Random Forest on the training dataset to obtain the predicted probabilities for all of the observations; Step 2: eliminate all negative examples that have a predicted probability smaller than p ; Alternatively, eliminate the top p % of observations with the lowest predicted probabilities. Obtain a subset S of the negative class; Step 3: randomly under-resample k times from S , to obtain balanced dataset B 1 , B 2 … B k ; Step 4: run the Random Forest on B 1 , B 2 … B k to build k Random Forests; and Step 5: combine the k Random Forests in Step 4 by voting to obtain the final model. In our implementation, we eliminate 15% of majority observations with the lowest predicted probabilities and we choose k = 7 . Results We report four different metrics of the classification models: (1) the sensitivity, or the true-positive rate; (2) the specificity, or the true-negative rate; (3) the accuracy, which is the ratio between the number of corrected predictions over the total predictions; and (4) the balanced accuracy, which is the average of the sensitivity and specificity. Since the goal of imbalanced data classification is to increase the sensitivity without reducing significantly the specificity, we use the balanced accuracy as the main metric to measure the overall performance of the classification models and resampling techniques. Our detailed results are reported in Tables 1 to 6 and illustrated in Figs 5, 6, and 7. Table 1. Random Forest Performance Metrics on Original Dataset and Five Resampling Methods. Original Under Over SMOTE ROSE SUR Accuracy 0.877 0.842 0.88 0.873 0.842 0.87 Sensitivity 0.605 0.791 0.726 0.723 0.774 0.774 Specificity 0.965 0.858 0.93 0.922 0.864 0.901 Balanced accuracy 0.785 0.825 0.828 0.822 0.819 0.837 Table 2. Performance Metrics of Six Classification Methods Running on the Original Dataset. Decision Tree Random Forest Logistic Regression KNN SVM AdaBoost Accuracy 0.877 0.877 0.879 0.739 0.877 0.88 Sensitivity 0.62 0.605 0.63 0.274 0.612 0.633 Specificity 0.961 0.965 0.959 0.889 0.963 0.96 Balanced accuracy 0.79 0.785 0.795 0.581 0.787 0.796 Table 3. Performance Metrics of Six Classification Methods Running on the Under-resampling Dataset. Decision Tree Random Forest Logistic Regression KNN SVM AdaBoost Accuracy 0.835 0.842 0.843 0.606 0.857 0.833 Sensitivity 0.753 0.791 0.774 0.637 0.751 0.801 Specificity 0.862 0.858 0.866 0.595 0.891 0.844 Balanced accuracy 0.807 0.825 0.82 0.616 0.821 0.822 Table 4. Performance Metrics of Six Classification Methods Running on the Over-resampling Dataset. Decision Tree Random Forest Logistic Regression KNN SVM AdaBoost Accuracy 0.836 0.88 0.847 0.588 0.864 0.84 Sensitivity 0.75 0.726 0.769 0.649 0.753 0.785 Specificity 0.864 0.93 0.872 0.568 0.9 0.857 Balanced accuracy 0.807 0.828 0.821 0.608 0.826 0.821 Table 5. Performance Metrics of Six Classification Methods Running on the SMOTE Dataset. Decision Tree Random Forest Logistic Regression KNN SVM AdaBoost Accuracy 0.829 0.873 0.833 0.597 0.866 0.86 Sensitivity 0.737 0.723 0.773 0.668 0.735 0.734 Specificity 0.859 0.922 0.852 0.574 0.909 0.901 Balanced accuracy 0.798 0.822 0.813 0.621 0.822 0.817 Table 6. Performance Metrics of Six Classification Methods Running on the ROSE Dataset. Decision Tree Random Forest Logistic Regression KNN SVM AdaBoost Accuracy 0.829 0.842 0.846 0.596 0.86 0.838 Sensitivity 0.729 0.774 0.763 0.572 0.746 0.788 Specificity 0.862 0.864 0.873 0.604 0.897 0.854 Balanced accuracy 0.795 0.819 0.818 0.588 0.821 0.821 Opens in a new window.Fig. 5. Balanced Accuracy of Six Classification Methods Where “rpart,” “ranger,” and “lr” Represent the Decision Tree, the Random Forest, and Logistic Regression, Respectively. Opens in a new window.Fig. 6. Balanced Accuracy of the Random Forest Running on the Original Dataset and Five Resampling Datasets. Opens in a new window.Fig. 7. Sensitivity of Six Classification Methods Where “rpart,” “ranger,” and “lr” Represent the Decision Tree, Random Forest, and Logistic Regression, Respectively. We observe that all classification methods have difficulty in classifying the positive observations. In particular, the KNN achieves the lowest sensitivity with only about 27%. Fig. 7 shows that with the help of the resampling methods, all classification methods are able to improve the true-positive rate with KNN being the one that most significantly improves (Table 7). Table 7. The Balanced Accuracy of Six Classification Models on the Original Dataset and Four Resampling Methods. Original Under Over SMOTE ROSE Decision tree 0.79 0.807 0.807 0.797 0.795 Random Forest 0.784 0.824 0.828 0.822 0.818 Logistic regression 0.794 0.819 0.82 0.812 0.818 KNN 0.581 0.616 0.608 0.62 0.587 SVM 0.787 0.82 0.826 0.821 0.821 AdaBoost 0.796 0.82 0.821 0.817 0.82 Fig. 5 also shows that all the resampling techniques help improve the overall performance of the classification models. While the decision tree, the Random Forest, the SVM, and AdaBoost give very similar good performances, KNN does not perform as well. Excluding SUR, the Random Forest is the best classification model. Combined with random over-sampling method, the Random Forest provides the best overall performance with the balanced accuracy of 0.8282. In addition, the average balanced accuracy of the Random Forest combined with four other resampling methods is also the highest: 0.8157. Of the six classification models, KNN gives the lowest average balanced accuracy (0.6028). Our method, SUR, does further improve the overall performance of the Random Forest, and it is better in doing so than the other four resampling techniques. Overall, SUR achieves the highest balanced accuracy, 0.837 (Table 1) Conclusion In this work, we study the performances of multiple classification methods on an imbalanced dataset with and without applying resampling techniques. Our computations have shown the effectiveness of applying resampling methods on imbalanced data before the modeling stage. All of the resampling techniques help improve the overall performances of the classification models. 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Book Chapters Prelims Section A Marketing, Sales, and Service Forecasting Exploring the Suitability of Support Vector Regression and Radial Basis Function Approximation to Forecast Sales of Fortune 500 Companies Buy-online-and-pick-up-in-store Strategy and Showroom Strategy in the Omnichannel Retailing Service Contracts for Delays in Delivery Section B Economic, Financial, and Insurance Forecasting Growth, Business Cycles, and the Great Recession: Comparing State and County Unemployment Costs Per Capita for North Carolina Dimension Reduction in Bankruptcy Prediction: A Case Study of North American Companies Detecting Non-injured Passengers and Drivers in Car Accidents: A New Under-resampling Method for Imbalanced Classification Section C CEO Compensation and Operations Forecasting Regression Modeling of the Peer Group of Verizon Corporation for the CEO of Verizon Regression Modeling Based on a Peer Group for the Executive Compensation of AT&T CEO Agent-based Queuing Model for Call Center Forecasting and Management Optimization Index
In an omnichannel environment, customers switch channels from product discovery to eventual purchase decision strategically. Hence, the biggest challenge for retailers nowadays is how to operate an effective omnichannel strategy. To improve inventory operational efficiency, this chapter investigates the influences of price setting and customers’ return probability on inventory forecasting. Subsequently, we explore how retailers participate in providing appropriate information delivery and product fulfillment. Specifically, a stylized newsvendor model, which incorporates customers’ showrooming behavior, is developed to address retailers’ inventory problem. Furthermore, we compare the benefits of buy-online-and-pick-up-in-store (BOPS) and showroom strategy which originates offline but is completed online. Three main findings are obtained as follows: (1)online and offline inventory order quantities augment with the ascending of pricing offline and online, respectively. Meanwhile, the inventory decisions increase when customers’ return probability declines; (2) the implementation of showroom helps retailers expand their pure online market coverage than BOPS, while it reduces the total inventory quantity if the proposition of unit online inventory cost accounting for product price exceeds physical store; and (3) showroom strategy is more profitable than BOPS option as long as unit online inventory cost is small enough. In addition, we find this boundary where showroom increases total profit expands with the attenuating of return probability.
Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.
This chapter develops a regression model structure based on the 34 peer companies of Verizon and their associated financial performance variables. Based on the regression model developed, the compensation level of the CEO of Verizon is determined.
