Identifying patterns of activities within individuals’ time diaries and studying similarities and deviations between individuals in a population is of interest in time use research. So far, activity patterns in a population have mostly been studied either by visual inspection, searching for occurrences of specific activity sequences and studying their distribution in the population, or statisti...

fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

Temporal pattern provides a novel way to character user behavior in social network from the perspective of time. In this work, we study two types of temporal pattern of user behavior in Micro-blog: Long Term Pattern and Daily Pattern. Long Term Pattern stands for the overall trend of user behavior changes since one starts to use Micro-blog and it provides the global view of user behavior variat...

Pattern matching in stock time series is an active research area in data mining. We propose a new real-time hybrid pattern-matching algorithm in this paper. The algorithm is based on the Spearman’s rank correlation, rule sets and sliding window. The concept of sliding windows enables patterns matching to be performed only based on subsequence of stock data which are freshly received. Therefore ...

Recently Kubica et al. (Inf. Process. Let., 2013) and Kim et al. (submitted to Theor. Comp. Sci.) introduced order-preserving pattern matching. In this problem we are looking for consecutive substrings of the text that have the same “shape” as a given pattern. These results include a linear-time order-preserving pattern matching algorithm for polynomially-bounded alphabet and an extension of th...

Sequential pattern mining is the mining of data sequences for frequent sequential patterns with time sequence, which has a wide application. Data streams are streams of data that arrive at high speed. Due to the limitation of memory capacity and the need of real-time mining, the results of mining need to be updated in real time. Multiple data streams are the simultaneous arrival of a plurality ...

Distributed Aircraft maintenance Environment (DAME) is a major UK eScience Grid project aiming to build a Grid test-bed for Distributed Diagnostics. The main challenge of the DAME project is the design and implementation of a fault diagnosis and prognosis system over the Grid computing infrastructure. This paper examines how the Grid may effect the implementation of search engines by focusing o...

The distribution of ordinal patterns in time series has been found to reflect important qualitative features of the underlying system dynamics. Abrupt changes in the dynamics typically result in clearly visible differences between the distributions before and after the break. Recurring dynamical regimes can be discovered by classifying the distributions in different parts of the time series. Th...

This paper presents a novel approach to find patterns in vehicle x-y-z acceleration data for use in prognostics and diagnostics. In this problem, vehicles are assumed to travel on the same routes and often times as a part of convoys but their GPS and other position information has been removed for privacy reasons. The goal of the pattern matching scheme is to identify the route or convoy associ...