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...

Time series segmentation is a fundamental component in the process of analyzing and mining time series data. Given a set of pattern templates, evolutionary computation is an appropriate tool to segment time series flexibly and effectively. In this paper, we propose a new distance measure based on pattern distance for fitness evaluation. Time sequence is represented by a series of perceptually i...

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...

water quality is a worldwide problem which affects human beings lives fundamentally. water scarcity is intensified in result of quality deterioration. different factors such as population increase, economic development and water pollution could be considered as the origins of the problem. the study and forecasting of water quality is necessary to prevent serious water quality deteriorations in ...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

in this research, the correlation between precipitation change in southern coasts of caspian sea and variability of sea level pressure have been analyzed by using environmental to circulation approach at time period 1339- 1383. in this respect, we make use of daily precipitation data of the 12 synoptic stations of studied area at surface data base section and prepare the time series matrix of p...

background road traffic accidents and their related deaths have become a major concern, particularly in developing countries. iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. in this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying arima models on data...