Operations Research, Systems Engineering and Industrial Engineering Commons^™

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez

Senior Design Project For Engineers

Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …

Radiology Associates Medical Scan Forecasting, Everett M. Notaro, Mitchell Carpenter, Tyler Deis, Kimberly Williams

Industrial and Manufacturing Engineering

After discontinuing their subscription with Shinyapps and relying on a manual forecasting process, Radiology Associates needs a new method to forecast the number and types of scans that will be executed at each site location. Radiology Associates utilizes Quinsite, which incorporates a live link to their database, as a host for all their Tableau dashboards. This project will create an accurate forecasting model utilizing complex forecasting methods to be hosted by Quinsite which is accessible by all management within Radiology Associates. To begin this process, an exponential smoothing model was created in Tableau to solidify dashboard and storyboard design. Additionally, …

Predicting F-16 Cause Code H Micap Hours Using Jmp Regression Analysis, Scott E. Carr

Theses and Dissertations

Emergency demands for aircraft parts, MICAPs (Mission Impaired Capability Awaiting Parts) are one of the leading issues affecting mission capability supply rates for fighter aircraft. Cause code H MICAPS, those with a known demand level, but no supply available, are particularly troublesome and difficult to resolve. These MICAPS are due to failures by the supply chain to replenish stock levels within acceptable time limits. Numerous studies have identified a clear need for proactive measures to reduce MICAP hours. This would significantly improve aircraft availability. This study uses regression analysis implemented in JMP software to build models with a goal of …

Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine

Faculty Publications

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in …

Database Analysis To Improve U.S. Transportation Command Forecasting Processes, Maxwell C. Thompson

Theses and Dissertations

The United States Transportation Command (USTRANSCOM) facilitates air, land, and sea transportation for the DOD. On a periodic basis, a myriad of different agencies within USTRANSCOM project future workload to facilitate resource planning, budgeting, and reimbursable rate identification. Within USTRANSCOM, there are a variety of databases and metrics utilized for workload forecasts; neither a standard nor a preferred technique is prescribed. Currently, USTRANSCOM faces challenges in producing accurate workload forecasts [1]. These challenges can lead to unreliable budget requests and, ultimately, hinder the effectiveness and efficiency of USTRANSCOM [1]. For the purpose of routine aircraft movements of cargo and personnel, …

Analysis And Forecasting Of The 360th Air Force Recruiting Group Goal Distribution, Tyler Spangler

Theses and Dissertations

This research utilizes monthly data from 2012-2017 to determine economic or demographic factors that significantly contribute to increased goaling and production potential in areas of the 360th Recruiting Groups. Using regression analysis, a model of recruiting goals and production is built to identify squadrons within the 360 RCGs zone that are capable of producing more or fewer recruits and the factors that contribute to this increased or decreased capability. This research identifies that a zones high school graduation rate, the number of recruiters, and the number of JROTC detachments in a zone are positively correlated with recruiting goals and that …

Ultra-Short-Term Wind Power Forecasting Based On Ceemd And Chaos Theory, Lijie Wang, Zhang Li, Zhang Yan

Journal of System Simulation

Abstract: This paper studies the ultra-short-term prediction of wind power generating capacity by means of CEEMD and chaos theory. Wind power time series is decomposed by CEEMD to decrease the non-stationary of time series. CEEMD can overcome the modal aliasing problem of EMD. The phase space reconstruction method is used to extract characteristics of each sequence, which provides the basis for the selection of input dimension when building a model. The least squares support vector machine models are built for each sequence and the prediction are made separately. The predicted results are added to get the final prediction. Simulation is …

Julian's Forecasting, Krista R. Bowdle

Industrial and Manufacturing Engineering

This report focuses on developing a solution to a current problem faced at Julian’s Cafe and Bistro. The problem is that inaccurate order quantities are leading to inventory shortage and surplus which is causing unnecessary profit loss. After accessing and analyzing historical sales data, several forecasts were created using various approaches. After assessing each method and applying a mean absolute percentage error to see how accurate the forecasts were, the seasonal forecast with removing outliers initially was the selected method. After, this method was used to forecast the top three selling items at Julian’s and make demand predictions for busy …

Analyzing The Fundamental Aspects And Developing A Forecasting Model To Enhance The Student Admission And Enrollment System Of Msom Program, Sultanul Nahian Hasnat

Graduate Theses and Dissertations

A forecasting model, associated with predictive analysis, is an elementary requirement for academic leaders to plan course requirements. The M.S. in Operations Management (MSOM) program at the University of Arkansas desires to understand future student enrollment more accurately. The available literature shows that there is an absence of forecasting models based on quantitative, qualitative and predictive analysis. This study develops a combined forecasting model focusing on three admission stages. The research uses simple regression, Delphi analysis, Analysis of Variance (ANOVA), and classification tree system to develop the models. It predicts that 272, 173, and 136 new students will apply, matriculate …

Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi

Electrical and Computer Engineering Faculty Research & Creative Works

Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying …

Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent's education status, and time since high school graduation. These inputs and the multi-layer neural network model are used …

Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. Therefore, the main purpose of this …

Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing …

An Examination Of Economic Metrics As Indicators Of Air Force Retention, Helen L. Jantscher

Theses and Dissertations

Fluctuations in the economy can cause military recruitment and retention plans to go awry. By focusing on various economic metrics, it is possible to anticipate changes in retention rates for specific Air Force Specialty Codes (AFSCs). To address the challenge of maintaining a robust and mission capable Air Force, a correlation analysis is employed to determine the relationship between certain economic indicators and AFSC retention rates. As one might suspect, retention rates follow the trend of decreasing when the economy is strong. Of interest, we found two AFSCs which go against this trend. Namely, the retention rates for officers in …

Optimizing Forecasting Methods For Ustranscom Railcar Demands, James M. Park

Theses and Dissertations

The United States military heavily relies on rail freight operations to meet many of its logistical needs during both peacetime and wartime efforts. As the head organization responsible for managing and overseeing all modes of military transportation, United States Transportation Command depends on timely accurate railcar demand forecasts to drive critical decisions on distribution and placement of railcar assets. However, the intermittent nature of railcar demands based on location and commodity make it a challenging task for forecasters. Furthermore, these “lumpy” demands often come without any obvious trends or seasonality. This study explores the utility of both traditional forecasting methods …

A Model Of Ambient Noise Caused By Wind Flow, Jovan Popovich

Theses and Dissertations

The generation of noise caused by wind owing past a human ear is an important yet vastly understudied factor in determining the ambient noise of an environment experienced by a human observer. Sound level measurements were obtained from wind tunnel tests simulating a human experiencing wind ows at various speeds and from various directions. This data set was used in this thesis. This thesis presents a collection of models for predicting wind noise levels across a broad spectrum of frequencies based on wind speed and angle inputs. Graphical approaches are included to characterize the observed data and illustrate the models' …

Aircraft Demand Forecasting, Kayla M. Monahan

Masters Theses

This thesis aims to forecast aircraft demand in the aerospace and defense industry, specifically aircraft orders and deliveries. Orders are often placed by airline companies with aircraft manufacturers, and then suddenly canceled due to changes in plans. Therefore, at some point during the three-year lead time, the number of orders placed and realized deliveries may be quite different. As a result, orders and deliveries are very difficult to predict and are influenced by many different factors. Among these factors are past trends, macroeconomic indicators as well as aircraft sales measures. These predictor variables were analyzed thoroughly, then used with time …

Oled Tv Technology Forecasting Using Technology Mining And The Fisher-Pry Diffusion Model, Yonghee Cho, Tugrul U. Daim

Joseph Cho

Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold …

Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu

Research Collection School Of Computing and Information Systems

As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …

A Predictive Logistic Regression Model Of World Conflict Using Open Source Data, Benjamin C. Boekestein

Theses and Dissertations

Nations transitioning into conflict is an issue of national interest. This study considers various data for inclusion in a statistical model that predicts the future state of the world where nations will either be in a state of violent conflict or not in violent conflict based on available historical data. Logistic regression is used to construct and test various models to produce a parsimonious world model with 15 variables. Further analysis shows that nations differ significantly by geographical area. Therefore six sub-models are constructed for differing geographical areas of the world. The dominant variables for each sub-model vary, suggesting a …

Using Earned Value Data To Forecast The Duration Of Department Of Defense (Dod) Space Acquisition Programs, Shedrick M. Bridgeforth

Theses and Dissertations

The accuracy of cost estimates is vital during this era of budget constraints. A key component of this accuracy is regularly updating the cost estimate at completion (EAC). A 2014 study by the Air Force Cost Analysis Agency (AFCAA) improved the accuracy of the cost estimate at completion (EAC) for space system contracts. The study found schedule duration to be a cost driver, but assumed the underlying duration estimate was accurate. This research attempts to improve the accuracy of the duration estimate from the AFCAA study; accuracy is evaluated with the Mean Absolute Percent Error (MAPE). The methods researched here …

Predicting Solar Irradiance Using Time Series Neural Networks, Ahmad Alzahrani, Jonathan W. Kimball, Cihan H. Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other …

Applying Inventory Control Practices Within The Sisters Of Mercy Health Care Supply Chain, Server Apras

Graduate Theses and Dissertations

This research lays a foundation for the better understanding of the application and acceptance of more advanced inventory control practices within the health care supply chain. The demand characteristics and optimal control policies for pharmaceutical items within a multi-echelon provider network are examined within the framework of a case study. Demand forecasting algorithms were applied to forecast demand for inventory control procedures. A spreadsheet-based inventory planning tool was used to minimize the inventory holding and ordering costs subject to fill rate constraints. The costs of inventory control models are compared to the current ordering and inventory control strategies to document …

Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin

Theses and Dissertations

Minimum distance estimate is a statistical parameter estimate technique that selects model parameters that minimize a good-of-fit statistic. Minimum distance estimation has been demonstrated better standard approaches, including maximum likelihood estimators and least squares, in estimating statistical distribution parameters with very small data sets. This research applies minimum distance estimation to the task of making time series predictions with very few historical observations. In a Monte Carlo analysis, we test a variety of distance measures and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when …

Forecasting Market Index Performance Using Population Demographics, Bradley J. Alden

Theses and Dissertations

This study attempted to verify claims made by forecaster Harry S. Dent, Jr. It utilized regression analysis in order to determine the correlation between the number of births and the closings on a market index with a specified lag between the input and output variables. While the research was able to develop a model with the factor Dent considers the most important, the predictions based on this model did not completely coincide with the forecasts Dent makes. Furthermore, the research could not confirm the accentuation Dent places on a 46-year lag between predictor and response variables. As an extension, scenarios …