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Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

2019

Operations Research, Systems Engineering and Industrial Engineering

Neural networks

Articles 1 - 6 of 6

Full-Text Articles in Engineering

Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke Mar 2019

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

David Lee Enke

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 ...


Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos Mar 2019

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

David Lee Enke

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 ...


Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke Mar 2019

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

David Lee Enke

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 ...


Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali Mar 2019

Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali

Cihan H. Dagli

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural ...


Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli Mar 2019

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

Cihan H. Dagli

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 ...


Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli Mar 2019

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

Steven Corns

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 ...