3,476 research outputs found

    Modelling financial time series with switching state space models

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    The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods

    Investigations on number selection for finite mixture models and clustering analysis.

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    by Yiu Ming Cheung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 92-99).Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.1.1 --- Bayesian YING-YANG Learning Theory and Number Selec- tion Criterion --- p.5Chapter 1.2 --- General Motivation --- p.6Chapter 1.3 --- Contributions of the Thesis --- p.6Chapter 1.4 --- Other Related Contributions --- p.7Chapter 1.4.1 --- A Fast Number Detection Approach --- p.7Chapter 1.4.2 --- Application of RPCL to Prediction Models for Time Series Forecasting --- p.7Chapter 1.4.3 --- Publications --- p.8Chapter 1.5 --- Outline of the Thesis --- p.8Chapter 2 --- Open Problem: How Many Clusters? --- p.11Chapter 3 --- Bayesian YING-YANG Learning Theory: Review and Experiments --- p.17Chapter 3.1 --- Briefly Review of Bayesian YING-YANG Learning Theory --- p.18Chapter 3.2 --- Number Selection Criterion --- p.20Chapter 3.3 --- Experiments --- p.23Chapter 3.3.1 --- Experimental Purposes and Data Sets --- p.23Chapter 3.3.2 --- Experimental Results --- p.23Chapter 4 --- Conditions of Number Selection Criterion --- p.39Chapter 4.1 --- Alternative Condition of Number Selection Criterion --- p.40Chapter 4.2 --- Conditions of Special Hard-cut Criterion --- p.45Chapter 4.2.1 --- Criterion Conditions in Two-Gaussian Case --- p.45Chapter 4.2.2 --- Criterion Conditions in k*-Gaussian Case --- p.59Chapter 4.3 --- Experimental Results --- p.60Chapter 4.3.1 --- Purpose and Data Sets --- p.60Chapter 4.3.2 --- Experimental Results --- p.63Chapter 4.4 --- Discussion --- p.63Chapter 5 --- Application of Number Selection Criterion to Data Classification --- p.80Chapter 5.1 --- Unsupervised Classification --- p.80Chapter 5.1.1 --- Experiments --- p.81Chapter 5.2 --- Supervised Classification --- p.82Chapter 5.2.1 --- RBF Network --- p.85Chapter 5.2.2 --- Experiments --- p.86Chapter 6 --- Conclusion and Future Work --- p.89Chapter 6.1 --- Conclusion --- p.89Chapter 6.2 --- Future Work --- p.90Bibliography --- p.92Chapter A --- A Number Detection Approach for Equal-and-Isotropic Variance Clusters --- p.100Chapter A.1 --- Number Detection Approach --- p.100Chapter A.2 --- Demonstration Experiments --- p.102Chapter A.3 --- Remarks --- p.105Chapter B --- RBF Network with RPCL Approach --- p.106Chapter B.l --- Introduction --- p.106Chapter B.2 --- Normalized RBF net and Extended Normalized RBF Net --- p.108Chapter B.3 --- Demonstration --- p.110Chapter B.4 --- Remarks --- p.113Chapter C --- Adaptive RPCL-CLP Model for Financial Forecasting --- p.114Chapter C.1 --- Introduction --- p.114Chapter C.2 --- Extraction of Input Patterns and Outputs --- p.115Chapter C.3 --- RPCL-CLP Model --- p.116Chapter C.3.1 --- RPCL-CLP Architecture --- p.116Chapter C.3.2 --- Training Stage of RPCL-CLP --- p.117Chapter C.3.3 --- Prediction Stage of RPCL-CLP --- p.122Chapter C.4 --- Adaptive RPCL-CLP Model --- p.122Chapter C.4.1 --- Data Pre-and-Post Processing --- p.122Chapter C.4.2 --- Architecture and Implementation --- p.122Chapter C.5 --- Computer Experiments --- p.125Chapter C.5.1 --- Data Sets and Experimental Purpose --- p.125Chapter C.5.2 --- Experimental Results --- p.126Chapter C.6 --- Conclusion --- p.134Chapter D --- Publication List --- p.135Chapter D.1 --- Publication List --- p.13

    DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

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    During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty

    Machine-assisted translation by Human-in-the-loop Crowdsourcing for Bambara

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    Language is more than a tool of conveying information; it is utilized in all aspects of our lives. Yet only a small number of languages in the 7,000 languages worldwide are highly resourced by human language technologies (HLT). Despite African languages representing over 2,000 languages, only a few African languages are highly resourced, for which there exists a considerable amount of parallel digital data. We present a novel approach to machine translation (MT) for under-resourced languages by improving the quality of the model using a paradigm called ``humans in the Loop.\u27\u27 This thesis describes the work carried out to create a Bambara-French MT system including data discovery, data preparation, model hyper-parameter tuning, the development of a crowdsourcing platform for humans in the loop, vocabulary sizing, and segmentation. We present a novel approach to machine translation (MT) for under-resourced languages by improving the quality of the model using a paradigm called ``humans in the Loop.\u27\u27 We achieved a BLEU (bilingual evaluation understudy) score of 17.5. The results confirm that MT for Bambara, despite our small data set, is viable. This work has the potential to contribute to the reduction of language barriers between the people of Sub-Saharan Africa and the rest of the world

    Developing a distributed electronic health-record store for India

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    The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

    Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

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    The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
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