Efficient and interpretable classification of time series is an essential data mining task with many real-world applications. Recently several dictionary- shapelet-based methods have been proposed that employ contiguous subsequences fixed length. We extend pattern to efficiently enumerate long variable-length sequential patterns gaps. Additionally, we discover at multiple resolutions thereby co...

Online detecting special patterns over financial data streams is an interesting and significant work. Existing many algorithms take it as a subsequence similarity matching problem. However, pattern detection on streaming time series is naturally expensive by this means. An efficient segmenting algorithm ONSP (ONline Segmenting and Pruning) is proposed, which is used to find the end points of sp...

In Taiwan stock market, it has been accumulated large amounts of time series stock data and successful investment strategies. The stock price, which is impacted by various factors, is the result of buyer-seller investment strategies. Since the stock price reflects numerous factors, its pattern can be described as the strategies of investors. In this paper, pattern recognition concept is adapted...

There is a considerable body of work on sequence mining of Web Log Data We are using One Pass frequent Episode discovery (or FED) algorithm, takes a different approach than the traditional apriori class of pattern detection algorithms. In this approach significant intervals for each Website are computed first (independently) and these interval used for detecting frequent patterns/Episode and th...

In agriculture, soil and meteorological sensors are used along low power networks to capture data, which allows for optimal resource usage and minimizing environmental impact. This study uses time series analysis methods for outliers' detection and pattern recognition on soil moisture sensor data to identify irrigation and consumption patterns and to improve a soil moisture prediction and irrig...

Recently, there has been much interest in designing and analyzing estimators for information theoretic functionals of probability density functions, under the assumption of independent and identically distributed (IID) data. However, estimators designed for IID data fail to capture relevant information when presented with time series data. We first present information theoretic functionals whic...

Lanzhou New District is the first and largest national-level new district in the Loess Plateau region of China. Large-scale land creation and rapid utilization of the land surface for construction has induced various magnitudes of land subsidence in the region, which is posing an increasing threat to the built environment and quality of life. In this study, the spatial and temporal evolution of...

This paper examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on 33-years of time series data compiled from satellite images. Along with new developments within the city fringes and rural villages in the valley, shifts in the natural environment and newly developed socioeconomic strains between resident...

The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the spatiotemporal features of the coarse-scale LST time series with greater spatial detail. This work concerns this issue and exploits the high temporal...