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Full-Text Articles in Physical Sciences and Mathematics
Analysis Of Break-Points In Financial Time Series, Jean Remy Habimana
Analysis Of Break-Points In Financial Time Series, Jean Remy Habimana
Graduate Theses and Dissertations
A time series is a set of random values collected at equal time intervals; this randomness makes these types of series not easy to predict because the structure of the series may change at any time. As discussed in previous research, the structure of time series may change at any time due to the change in mean and/or variance of the series. Consequently, based on this structure, it is wise not to assume that these series are stationary. This paper, discusses, a method of analyzing time series by considering the entire series non-stationary, assuming there is random change in unconditional …
A Multi-Indexed Logistic Model For Time Series, Xiang Liu
A Multi-Indexed Logistic Model For Time Series, Xiang Liu
Electronic Theses and Dissertations
In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare …
A Novel Recurrent Convolutional Neural Network For Ocean And Weather Forecasting, Robert James Firth
A Novel Recurrent Convolutional Neural Network For Ocean And Weather Forecasting, Robert James Firth
LSU Doctoral Dissertations
Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but also the creation of a parameterization scheme to estimate sub-grid scale phenomenon. The proposed method is an alternative approach to developing a mesoscale meteorological model – a modified recurrent convolutional neural network that learns to simulate the solution to these equations. Along with an appropriate time integration scheme and learning algorithm, this method can be used to create multi-day forecasts for a large region. The learning method presented is an extended form of Backpropagation Through …
Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus
Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus
Dissertations, Master's Theses and Master's Reports
The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which …