In technical systems the analysis of similar situations is a promising technique to gain information about system’s state, its health or wearing. Very often, cannot be defined but need discovered as recurrent patterns within time series data system under consideration. This paper addresses assessment different approaches discover frequent variable-length in series. Because success artificial ne...

We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in the data | the underlying process’s causal states. Unlike conventional methods for tting hidden Markov models (HMMs) to data, our algorithm makes...

Time-series prediction is important in physical and financial domains. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This paper describes a Pattern Modelling and Recognition System (PMRS) which is used for forecasting benchmark series a...

This paper addresses the issue of discovering key sequences from time series data for pattern classification. The aim is to find from a symbolic database all sequences that are both indicative and non-redundant. A sequence as such is called a key sequence in the paper. In order to solve this problem we first we establish criteria to evaluate sequences in terms of the measures of evaluation base...

We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix. By recasting the time series as a matrix it becomes possible to use SVD to highlight the underlying patterns and periodicities. This is done without t...

We aimed to study the clinical pattern of nocardiosis in a tertiary care hospital in Saudi Arabia using a retrospective review of cases from 1987 to 2003. A total of 19 patients were identified as having culture-proven nocardial infection. The most common underlying condition was renal transplantation in 8 patients (42%). Lungs were the most common sites of involvement in 12 patients (63%). Thr...

A fundamental element of future generic pattern recognition technology is the ability to extract similar patterns for the same scene despite wide ranging extraneous variables, including lighting, turbidity, sensor exposure variations, and signal noise. In the process of demonstrating pattern constancy of this kind for retinex/visual servo (RVS) image enhancement processing, we found that the pa...

Mining of time series pattern is an important research area, of which getting LCSS(Longest Common Subsequence) between high-dimensional time series is one of the most important issues. Large scale data needs to be handled in practical applications, so the research of efficient retrieval method is becoming a realistic work. Based on the issues above, we propose an efficient parallel algorithm to...

Description Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models License GPL-2 Index 8 ar1analysis Performs AR1 fitting Description Calculation of the autocorrelation coefficients genes and variance of corresponding random variables to fit gene expression time series by AR1 processes...