Tugba Taskaya-Temizel

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Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each(More)
Most financial time series processes are nonstationary and their frequency characteristics are time-dependant. In this paper we present a time series summarization and prediction framework to analyse volatile and high-frequency time series data. Multiscale wavelet analysis is used to separate out the trend, cyclical fluctuations and autocorrelational(More)
Time series often exhibit periodical patterns that can be analysed by conventional statistical techniques. These techniques rely upon an appropriate choice of model parameters that are often difficult to determine. Whilst neural networks also require an appropriate parameter configuration, they offer a way in which non-linear patterns may be modelled.(More)
The incorporation of pharmacogenomics information into the drug dosing estimation formulations has been shown to increase the accuracy in drug dosing and decrease the frequency of adverse drug effects in many studies in the literature. In this paper, an estimation framework based on the Bayesian structural equation modeling, which is driven by(More)