Displaying similar documents to “Computational intensive methods for prediction and imputation in time series analysis”

Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set

Áurea Sousa, Helena Bacelar-Nicolau, Fernando C. Nicolau, Osvaldo Silva (2013)

Biometrical Letters

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In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained...

Exponential smoothing and resampling techniques in time series prediction

Maria Manuela Neves, Clara Cordeiro (2010)

Discussiones Mathematicae Probability and Statistics

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Time series analysis deals with records that are collected over time. The objectives of time series analysis depend on the applications, but one of the main goals is to predict future values of the series. These values depend, usually in a stochastic manner, on the observations available at present. Such dependence has to be considered when predicting the future from its past, taking into account trend, seasonality and other features of the data. Some of the most successful forecasting...

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Ammar Shaker, Eyke Hüllermeier (2014)

International Journal of Applied Mathematics and Computer Science

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In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to...

Optimization of Parameters in the Menzerath–Altmann Law, II

Ján Andres, Martina Benešová, Martina Chvosteková, Eva Fišerová (2014)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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The paper continues our studies released under the same title [Andres, J., Kubáček, L., Machalová, J., Tučková, M.: Optimization of parameters in the Menzerath–Altmann law Acta Univ. Palacki. Olomuc., Fac. rer. nat., Math. 51, 1 (2012), 5–27.]. As the main result justifying the conclusions in [Andres, J., Kubáček, L., Machalová, J., Tučková, M.: Optimization of parameters in the Menzerath–Altmann law Acta Univ. Palacki. Olomuc., Fac. rer. nat., Math. 51, 1 (2012), 5–27.], the theorem...