Utilizing Recurrent Neural Networks for Temporal Data Generation and Prediction
| dc.contributor.author | Nguyen, Thaovy Tuong | en |
| dc.contributor.committeechair | Hewett, Russell Joseph | en |
| dc.contributor.committeemember | Martin, Eileen R. | en |
| dc.contributor.committeemember | Gugercin, Serkan | en |
| dc.contributor.department | Mathematics | en |
| dc.date.accessioned | 2021-06-16T08:00:24Z | en |
| dc.date.available | 2021-06-16T08:00:24Z | en |
| dc.date.issued | 2021-06-15 | en |
| dc.description.abstract | The Falling Creek Reservoir (FCR) in Roanoke is monitored for water quality and other key measurements to distribute clean and safe water to the community. Forecasting these measurements is critical for management of the FCR. However, current techniques are limited by inherent Gaussian linearity assumptions. Since the dynamics of the ecosystem may be non-linear, we propose neural network-based schemes for forecasting. We create the LatentGAN architecture by extending the recurrent neural network-based ProbCast and autoencoder forecasting architectures to produce multiple forecasts for a single time series. Suites of forecasts allow for calculation of confidence intervals for long-term prediction. This work analyzes and compares LatentGAN's accuracy for two case studies with state-of-the-art neural network forecasting methods. LatentGAN performs similarly with these methods and exhibits promising recursive results. | en |
| dc.description.abstractgeneral | The Falling Creek Reservoir (FCR) is monitored for water quality and other key measurements to ensure distribution of clean and safe water to the community. Forecasting these measurements is critical for management of the FCR and can serve as indicators of significant ecological events that can greatly reduce water quality. Current predictive techniques are limited due to inherent linear assumptions. Thus, this work introduces LatentGAN, a data-driven, generative, predictive neural network. For a particular sequence of data, LatentGAN is able to generate a suite of possible predictions at the next time step. This work compares LatentGAN's predictive capabilities with existing neural network predictive models. LatentGAN performs similarly with these methods and exhibits promising recursive results. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:31621 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/103874 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Neural Networks | en |
| dc.subject | LatentGAN | en |
| dc.subject | Falling Creek Reservoir | en |
| dc.title | Utilizing Recurrent Neural Networks for Temporal Data Generation and Prediction | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mathematics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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