Thesis (Ph. D.)--University of Wisconsin--Madison, 1968. / Typescript. Description based on print version record. Includes bibliographical references (leaves 212-216).

Thesis (Ph. D.)--Stockholm School of Economics, 1998. / Includes bibliographical references.

Thesis (Ph.D. in Civil and Environmental Engineering)--University of California, Davis, 2004. / Cover title. Computer-produced typeface. Includes bibliographical references (p. 149-161). Also available via the World Wide Web. (Restricted to UC campuses)

Thesis (M. Phil.)--University of Hong Kong, 2006.

Thesis (M.Sc.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 91-92.

Thesis (M.P.H.)--University of Hong Kong, 2005.

Thesis (Ph. D.)--University of California, San Diego, 2009. / Title from first page of PDF file (viewed September 17, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 234-241).

Master of Science / Department of Computer Science / William H. Hsu / Cryptocurrencies are digital currencies that have garnered significant investor attention in the financial markets. The aim of this project is to predict the daily price, particularly the daily high and closing price, of the cryptocurrency Bitcoin. This plays a vital role in making trading decisions. There exist various factors which affect the price of Bitcoin, thereby making price prediction a complex and technically challenging task. To perform prediction, we trained temporal neural networks such as time-delay neural networks (TDNN) and recurrent neural networks (RNN) on historical time series – that is, past prices of Bitcoin over several years. Features such as the opening price, highest price, lowest price, closing price, and volume of a currency over several preceding quarters were taken into consideration so as to predict the highest and closing price of the next day. We designed and implemented TDNNs and RNNs using the NeuroSolutions artificial neural network (ANN) development environment to build predictive models and evaluated them by computing various measures such as the MSE (mean square error), NMSE (normalized mean square error), and r (Pearson’s correlation coefficient) on a continuation of the training data from each time series, held out for validation.

Orientador: Claudia Maria Bauzer Medeiros / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-23T21:42:10Z (GMT). No. of bitstreams: 1 Silva_FelipeHenriquesda_M.pdf: 3283921 bytes, checksum: 6875b168c728390c5cbeb2e32389cb99 (MD5) Previous issue date: 2013 / Resumo: Séries temporais são sequências de valores medidos em sucessivos instantes de tempo. Elas são usadas em diversos domínios, tais como agricultura, medicina e economia. A análise dessas séries é de extrema importância, fornecendo a especialistas a capacidade de identificar tendências e prever possíveis cenários. A fim de facilitar sua análise, especialistas frequentemente associam anotações com séries temporais. Tais anotações também podem ser usadas para correlacionar séries distintas, ou para procurar por séries específicas num banco de dados. Existem muitos desafios envolvidos no gerenciamento destas anotações - desde encontrar estruturas adequadas para associá-las com as séries, até organizar e recuperar séries através das anotações associadas a estas. Este trabalho contribui para o trabalho em gerenciamento de séries temporais. Suas principais contribuições são o projeto e desenvolvimento de um arcabouço para o gerenciamento de múltiplas anotações associadas com uma ou mais séries em um banco de dados. Este arcabouço também fornece meios para o controle de versão das anotações, de modo que os estados anteriores de uma anotação nunca sejam perdidos. Serial Annotator é uma aplicação desenvolvida para a plataforma Android. Ela foi usada para validar o arcabouço proposto e foi testada com dados reais envolvendo problemas do domínio agrícola / Abstract: Time series are sequences of values measured at successive time instants. They are used in several domains such as agriculture, medicine and economics. The analysis of these series is of utmost importance, providing experts the ability to identify trends and forecast possible scenarios. In order to facilitate their analyses, experts often associate annotations with time series. Such annotations can also be used to correlate distinct series, or look for specific series in a database. There are many challenges involved in managing annotations - from finding proper structures to associate them with series, to organizing and retrieving series based on annotations. This work contributes to the work in management of time series. Its main contributions are the design and development of a framework for the management of multiple annotations associated with one or multiple time series in a database. The framework also provides means for annotation versioning, so that previous states of an annotation are never lost. Serial Annotator is an application implemented for the Android smart phone platform. It has been used to validate the proposed framework and has been tested with real data involving agriculture problems / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

In this thesis, we investigate the estimation problem and inference problem for the complex models. Two major categories of complex models are emphasized by us, one is generalized linear models, the other is time series models. For the generalized linear models, we consider one fundamental problem about sure screening for interaction terms in ultra-high dimensional feature space; for time series models, an important model assumption about Markov property is considered by us. The first part of this thesis illustrates the significant interaction pursuit problem for ultra-high dimensional models with two-way interaction effects. We propose a simple sure screening procedure (SSI) to detect significant interactions between the explanatory variables and the response variable in the high or ultra-high dimensional generalized linear regression models. Sure screening method is a simple, but powerful tool for the first step of feature selection or variable selection for ultra-high dimensional data. We investigate the sure screening properties of the proposal method from theoretical insight. Furthermore, we indicate that our proposed method can control the false discovery rate at a reasonable size, so the regularized variable selection methods can be easily applied to get more accurate feature selection in the following model selection procedures. Moreover, from the viewpoint of computational efficiency, we suggest a much more efficient algorithm-discretized SSI (DSSI) to realize our proposed sure screening method in practice. And we also investigate the properties of these two algorithms SSI and DSSI in simulation studies and apply them to some real data analyses for illustration. For the second part, our concern is the testing of the Markov property in time series processes. Markovian assumption plays an extremely important role in time series analysis and is also a fundamental assumption in economic and financial models. However, few existing research mainly focused on how to test the Markov properties for the time series processes. Therefore, for the Markovian assumption, we propose a new test procedure to check if the time series with beta-mixing possesses the Markov property. Our test is based on the Conditional Distance Covariance (CDCov). We investigate the theoretical properties of the proposed method. The asymptotic distribution of the proposed test statistic under the null hypothesis is obtained, and the power of the test procedure under local alternative hypothesizes have been studied. Simulation studies are conducted to demonstrate the finite sample performance of our test.