Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use o...

We consider modeling procedures for multiple time series which aim to address the challenge of providing both a good representation of the structure, and an efficient parameterization. We first review a method, applied to vector autoregressions of low order, which uses conditional independence graphs to identify a sparse structural autoregressive representation. We show by an example how this m...

SUMMARY The development of adequate models for binary time series data with covariate adjustment has been an active research area in the last years. In the case, where interest is focused on marginal and association parameters, generalized estimating equations (GEE) (see for example Lipsitz, Laird and Harrington (1991) and Liang, Zeger and Qaqish (1992)) and likelihood (see for example Fitzmaur...

one of the water resources modeling requirements is sufficient knowledge of long-term series of meteorological and hydrological parameters. in this study the nearest neighbor resampling method presented by lall and sharma was developed. in the developed model, the knn regression was used for time series forecasting instead of local polynomial used in the developed algorithm by prairie. in this ...

Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understan...

When dealing with multivariate time series an important question one may ask is whether the measured signals are independent or not. Any interrelation can, for example, provide information about the underlying physical mechanisms or may be used to exploit possible redundancy in order to reduce the number of measurement channels. In linear systems theory the standard tool for detecting dependenc...

Graphical models for multivariate time series is a concept extended by Dahlhaus (2000) from a random vector to a time series. We propose a test statistic to identify a graphical model for multivariate time series with the Kullback-Leibler distance between two spectral density matrices characterized by graphical models. Asymptotic null distribution is derived to be normal with the mean and varia...

Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the data mining community is the recognition of recurring patterns within temporal databases or streaming data. This dissertation proposal aims at developing and investigating efficient methods for the recognition of context...

Copula-based models provide a great deal of ‡exibility in modelling multivariate distributions, allowing the researcher to specify the models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. In addition to ‡exibility, this often also facilitates estimation of the model in stages, reducing the computational burden. Thi...

Supervised classiication is one of the most active areas of machine learning research. Most work has focused on classiication in static domains, where an instantaneous snapshot of attributes is meaningful. In many domains, attributes are not static; in fact, it is the way they vary temporally that can make classiication possible. Examples of such domains include speech recognition, gesture reco...