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dc.contributor.authorKhadivi, Pejmanen_US
dc.date.accessioned2016-09-09T08:00:12Z
dc.date.available2016-09-09T08:00:12Z
dc.date.issued2016-09-08en_US
dc.identifier.othervt_gsexam:8862en_US
dc.identifier.urihttp://hdl.handle.net/10919/72907
dc.description.abstractDealing with noisy time series is a crucial task in many data-driven real-time applications. Due to the inaccuracies in data acquisition, time series suffer from noise and instability which leads to inaccurate forecasting results. Therefore, in order to improve the performance of time series forecasting, an important pre-processing step is the denoising of data before performing any action. In this research, we will propose various approaches to tackle the noisy time series in forecasting applications. For this purpose, we use different machine learning methods and information theoretical approaches to develop online denoising algorithms. In this dissertation, we propose four categories of time series denoising methods that can be used in different situations, depending on the noise and time series properties. In the first category, a seasonal regression technique is proposed for the denoising of time series with seasonal behavior. In the second category, multiple discrete universal denoisers are developed that can be used for the online denoising of discrete value time series. In the third category, we develop a noisy channel reversal model based on the similarities between time series forecasting and data communication and use that model to deploy an out-of-band noise filtering in forecasting applications. The last category of the proposed methods is deep-learning based denoisers. We use information theoretic concepts to analyze a general feed-forward deep neural network and to prove theoretical bounds for deep neural networks behavior. Furthermore, we propose a denoising deep neural network method for the online denoising of time series. Real-world and synthetic time series are used for numerical experiments and performance evaluations. Experimental results show that the proposed methods can efficiently denoise the time series and improve their quality.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectData analyticsen_US
dc.subjectDenoisingen_US
dc.subjectInformation theoryen_US
dc.subjectTime seriesen_US
dc.subjectForecastingen_US
dc.titleOnline Denoising Solutions for Forecasting Applicationsen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePHDen_US
thesis.degree.namePHDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairRamakrishnan, Narendranen_US
dc.contributor.committeememberYao, Danfengen_US
dc.contributor.committeememberMarathe, Madhav Vishnuen_US
dc.contributor.committeememberTandon, Ravien_US
dc.contributor.committeememberPrakash, Bodicherla Adityaen_US


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