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Articles 1 - 4 of 4
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke
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
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
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
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 …