Displaying similar documents to “The dynamic linear model: An application to flood forecasting.”

Adaptive prediction of stock exchange indices by state space wavelet networks

Mietek A. Brdyś, Adam Borowa, Piotr Idźkowiak, Marcin T. Brdyś (2009)

International Journal of Applied Mathematics and Computer Science

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The paper considers the forecasting of the Warsaw Stock Exchange price index WIG20 by applying a state space wavelet network model of the index price. The approach can be applied to the development of tools for predicting changes of other economic indicators, especially stock exchange indices. The paper presents a general state space wavelet network model and the underlying principles. The model is applied to produce one session ahead and five sessions ahead adaptive predictors of the...

Modelling stock returns with AR-GARCH processes.

Elzbieta Ferenstein, Miroslaw Gasowski (2004)

SORT

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Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to...

Learning from imprecise examples with GA-P algorithms.

Luciano Sánchez, Inés Couso (1998)

Mathware and Soft Computing

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GA-P algorithms combine genetic programming and genetic algorithms to solve symbolic regression problems. In this work, we will learn a model by means of an interval GA-P procedure which can use precise or imprecise examples. This method provides us with an analytic expression that shows the dependence between input and output variables, using interval arithmetic. The method also provides us with interval estimations of the parameters on which this expression depends. The...

Weighting quantitative and qualitative variables in clustering methods.

Karina Gibert, Ulises Cortés (1997)

Mathware and Soft Computing

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Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required. In this paper properties and details of the metrics...

Knowledge discovery in data using formal concept analysis and random projections

Cherukuri Aswani Kumar (2011)

International Journal of Applied Mathematics and Computer Science

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In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.