The analysis of time series databases is very important in the area of medicine. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, index trees, etc. However, a domain-dependent analysis sometimes needs to be conducted to search for the symbolic rather than numerical characteristics of the time series. This paper focuses on our...

It has been widely observed that temporal semantics and functionality are often developed on an ad hoc basis, and the benefits of temporal databases research are rarely realised. In this paper we propose an independent temporal model, which embraces object oriented concepts and also show how UML can be used to model temporal business concepts.

It is widely held that the dinosaurs were driven to near extinction because of the Chicxulub asteroid collision with the Earth about 65 million years ago (MA). Without doubt, dinosaur diversity in the fossil record after the collision was at most a percent of what it was prior to the collision. But whether the collision was the principal cause of the extinction is more difficult to assess. Here...

——————————————————————————————————— It is common in parametric bootstrap to select the model from the data, and then treat it as it were the true model. Kilian (1998) have shown that ignoring the model uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for impulse response estimates which are closely related with multi-step-ahead prediction intervals. In...

With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all nece...

Time series occur throughout nature and within almost every discipline of science. Producing accurate alignments of time series data was made feasible with the Dynamic Time Warping algorithm. Through stretching and compressing of individual points in time series data, this algorithm produces accurate and intuitive global time series alignments. In this paper, we extend the DTW algorithm to perf...

We introduce a nonparametric nonlinear time series model. The novel idea is to fit a model via penalization, where the penalty term is an unbiased estimator of the integrated Hessian of the underlying function. The underlying model assumption is very general: it has Hessian almost everywhere in its domain. Numerical experiments demonstrate that our model has better predictive power: if the unde...

A new gait recognition algorithm, the layered time series model (LTSM), is proposed. LTSM is a two-level model which combines the dynamic texture model (DTM) and the hidden Markov model (HMM). A gait cycle is divided into several temporally adjacent clusters and gait features of each cluster are modelled by the DTM. The HMM is built to describe the relationship among the DTMs, which are regarde...

This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-world time-series data, which results from the dimension, nonlinearity, and nonstationarity of the generating process, as well as from measurement issues such as noise, aggregation, and finite data length, is both empir...

Accurate groundwater level modeling and forecasting contribute to civil projects, land use, citys planning and water resources management. Combined Wavelet-Artificial Neural Network (WANN) model has been widely used in recent years to forecast hydrological and hydrogeological phenomena. This study investigates the sensitivity of the pre-processing to the wavelet type and decomposition level in ...