Time Series Clustering Problem Research Articles

Extraction and Analysis of Crowd Activity Vergence Model in Space-Time Vector Field

The vergence model of crowd activity is one of the core contents of human mobility research. Traditional methods do not consider the mobility of crowd activities in terms of extracting vergence models. In this paper, the model extraction problem is transformed into a time series clustering problem, and the mobility of crowd activities is dynamically modeled by introducing vector field theory. Then, the vergence of the crowd is calculated by the divergence. Finally, a time series composed of the crowd vergence is constructed to obtain the main vergence model of crowd activity through clustering. The method proposed in this paper is experimentally verified on the Didi Chuxing data in Haikou City, and four main vergence models of the crowd activity are extracted, which proves that the method proposed in this paper is effective and provides research ideas and method support for exploring human mobility.

Metaheuristics on time series clustering problem: theoretical and empirical evaluation

Considering the literature and the importance of using of the metaheuristic techniques in time series data mining tasks, especially time series clustering (TsC), it seems lack of a comparative study of such techniques in terms of efficiency for TsC problem. Hence, we try to offer the possibility of theoretical and empirical evaluation of metaheuristic techniques in TsC problem. In fact, we follow two main goals by performing this theoretical and empirical evaluation. These goals include: at the first, we would like to prove the effective role of metaheuristic techniques to enhance the efficiency of TsC algorithms, eliminate some challenges of such algorithms (e.g., sensitivity to initialization of some primary parameters), and indicates the popularity of their use in TsC problems during the last years because of their characteristics. Second, we would like to offer the possibility of a comparison between some metaheuristic techniques to analyze the percentage of their effectiveness for enhancing the accuracy of clustering results in TsC problems. The comparative analysis of results of the empirical evaluation for ten standard time series data sets collected from the UCR time series data sets repository show three keys conclusions: (1) generally, metaheuristic techniques have provided reasonable results and significant improvement in terms of efficiency in TsC problem in all experiments due to the main characteristic of metaheuristics that is finding an approximate solution more quickly. But, it is concluded that fuzzy metaheuristic techniques based on population solution class (e.g., FATPSO and FPSO) to provide better results versus single solution class in TsC algorithms. (2) It is deducted that consuming maximum computational time presented by fuzzy metaheuristic techniques in comparison to other optimization techniques in the performed experiments. (3) The paper has proposed that a hybrid of fuzzy metaheuristics (e.g., FATPSO) and the base TsC algorithms can be considered as a more adequate choice to present better results in terms of accuracy in different application areas.

Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations

Consider the problem of clustering objects with temporally changing multivariate variables, for instance, in the classification of cities with several changing socioeconomic indices in geographical research. If the changing multivariate can be recorded simultaneously as a multivariate time series, in which the length of each subseries is equal and the subseries can be correlated, the problem is transformed into a multivariate time series clustering problem. The available methods consider the correlations between distinct time series but overlook the shape of each time series, which causes multivariate time series with similar correlations and opposite shapes to be clustered into the same class. To overcome this problem, this paper proposes a two-phase multivariate time series clustering algorithm that considers both correlation and shape. In Phase I, the discrete wavelet transform is applied to capture the wavelet variances and the correlation coefficients between each pair of variables to realize the initial clustering of multivariate time series, where time series with a similar correlation but opposite shape may be assigned to the same cluster. In Phase II, multivariate time series are clustered based on shape via the symbolic aggregate approximation (SAX) method. In this phase, time series with similar correlations but opposite morphologies are differentiated. The method is evaluated using multivariate time series of incoming and outgoing passenger volumes from Beijing IC card data; these volume data were collected between March 4, 2013 and March 17, 2013. Based on the silhouette coefficient, our approach outperforms two popular multivariate time series clustering methods: a wavelet-based method and the SAX method.

Time series clustering based on sparse subspace clustering algorithm and its application to daily box-office data analysis

Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task. Our study focuses on finding the regular box-office revenue patterns. Clustering algorithm is unsupervised machine learning algorithm which classifies the data in the absence of early knowledge of the classes. Unlike static data, the time series data vary with time. The work focused on time series clustering analysis is relatively less than those focused on static data. In this paper, the sparse subspace clustering (SSC) algorithm is introduced to analyze the time series data. The SSC algorithm has a better performance both on the artificial data set and the daily box-office data than recently developed well-known clustering algorithm such as K-means and spectral clustering algorithm. On the artificial data set, SSC is more suitable for time series, whether from the angle of clustering error or visualization. On the actual data, movies are divided into five clusters by SSC algorithm, and each cluster represents a distinct type of distribution pattern. And these patterns can be used in movie recommendation, film evaluation and can guide theater exhibitors and distributors. In addition, this is the first time to apply SSC to deal with time series clustering problem and get a pleasant effect.

Mining and modelling the dynamic patterns of service providers in cellular data network based on big data analysis

Understanding the dynamic traffic and usage characteristics of data services in cellular networks is important for optimising network resources and improving user experience. Recent studies have illustrated traffic characteristics from specific perspectives, such as user behaviour, device type, and applications. In this paper, we present the results of our study from a different perspective, namely service providers, to reveal the traffic characteristics of cellular data networks. Our study is based on traffic data collected over a five-day period from a leading mobile operator's core network in China. We propose a Zipf-like model to characterise the distributions of the traffic volume, subscribers, and requests among service providers. Nine distinct diurnal traffic patterns of service providers are identified by formulating and solving a time series clustering problem. Our work differs from previous related works in that we perform measurements on a large quantity of data covering 2.2 billion traffic records, and we first explore the traffic patterns of thousands of service providers. Results of our study present mobile Internet participants with a better understanding of the traffic and usage characteristics of service providers, which play a critical role in the mobile Internet era.

MDL-based time series clustering

Time series data are pervasive across all human endeavors, and clustering is arguably the most fundamental data mining application. Given this, it is somewhat surprising that the problem of time series clustering from a single stream remains largely unsolved. Most work on time series clustering considers the clustering of individual time series that have been carefully extracted from their original context, for example, gene expression profiles, individual heartbeats, or individual gait cycles. The few attempts at clustering time series streams have been shown to be objectively incorrect in some cases, and in other cases shown to work only on the most contrived synthetic datasets by carefully adjusting a large set of parameters. In this work, we make two fundamental contributions that allow for the first time, the meaningful clustering of subsequences from a time series stream. First, we show that the problem definition for time series clustering from streams currently used is inherently flawed, and a new definition is necessary. Second, we show that the minimum description length framework offers an efficient, effective, and essentially parameter-free method for time series clustering. We show that our method produces objectively correct results on a wide variety of datasets from medicine, speech recognition, zoology, gesture recognition, and industrial process analyses.

Flood Season Division with an Improved Fuzzy C-mean Clustering Method in the Taihu Lake Basin in China

The division of the flood season in a basin is a multi-dimension time series clustering problem with unknown clustering numbers. So the clustering method is required to have the capability of dealing with multi-dimensions, time series and clustering validity. A dynamic fuzzy C-means clustering method (simplified as DFCCM) with clustering validity function based time series was developed in the paper. The real coding based accelerating genetic algorithm (simplified as RAGA) is used for solving DFCCM, which is very effective and can overcome the initial value sensitivity from the regular iterative optimal method generally used for the fuzzy C-means clustering. The newly developed method was applied in the Taihu lake basin in China for the division of the basin flood season, and 11 schemes were designed for removing the uncertainty from the initial value of flood season, which could exert influence on the results of the flood seasons division. Application results show that the method is very effective and the division conclusion of flood season in the Taihu Lake Basin is also reasonable.

Time series clustering and classification by the autoregressive metric

The statistical properties of the autoregressive ( AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.