Clustering Of Time Series Data Research Articles

State‐space discrimination and clustering of atmospheric time series data based on Kullback information measures

AbstractStatistical problems in atmospheric science are frequently characterized by large spatio‐temporal data sets and pose difficult challenges in classification and pattern recognition. Here, we consider the problem of identifying geographically homogeneous regions based on similarities in the temporal dynamics of weather patterns. Two disparity measures are proposed and applied to cluster time series of observed monthly temperatures from locations across Colorado, U.S.A. The two disparity measures are based on state‐space models, where the monthly temperature anomaly dynamics and seasonal variation are represented by latent processes. Our disparity measures produce clusters consistent with known atmospheric flow structures. In particular, the temporal anomaly pattern is related to the topography of Colorado, where, separated by the Continental Divide, the flow structures in the western and eastern parts of the state have different dynamics. The results further suggest that seasonal variation may be affected by locally changing solar radiation levels primarily associated with elevation variations across the Rocky Mountains. The general methodology is outlined and developed in the Appendix. We conclude with a discussion of extensions to time varying and non‐stationary systems. Copyright © 2007 John Wiley & Sons, Ltd.

DWT–CEM: an algorithm for scale-temporal clustering in fMRI

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.

Characteristic-Based Clustering for Time Series Data

With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is critical for us to ...

Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation

Precious ecological information extracted from limnological long-term time series advances the theory on functioning and evolution of freshwater ecosystems. This paper presents results of applications of artificial neural networks (ANN) and evolutionary algorithms (EA) for ordination, clustering, forecasting and rule discovery of complex limnological time-series data of two distinctively different lakes. Ten years of data of the shallow and hypertrophic Lake Kasumigaura (Japan) are utilized in comparison with 13 years of data of the deep and mesotrophic Lake Soyang (Korea). Results demonstrate the potential that: (1) recurrent supervised ANN and EA facilitate 1-week-ahead forecasting of outbreaks of harmful algae or water quality changes, (2) EA discover explanatory rule sets for timing and abundance of harmful outbreaks algal populations, and (3) non-supervised ANN provide clusters to unravel ecological relationships regarding seasons, water quality ranges and long-term environmental changes.

Clustering of time series data—a survey

Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research.

Fast Clustering for Time-series Data with Average-time-sequence-vector Generation Based on Dynamic Time Warping.

This paper proposes a fast clustering method for time-series data based on average time sequence vector. A clustering procedure based on an exhaustive search method is time-consuming although its result typically exhibits high quality. BIRCH, which reduces the number of examples by data squashing based on a data structure CF (Clustering Feature) tree, represents an effective solution for such a method when the data set consists of numerical attributes only. For time-series data, however, a straightforward application of BIRCH based on a Euclidean distance for a pair of sequences, miserably fails since such a distance typically differs from human's perception. A dissimilarity measure based on DTW (Dynamic Time Warping) is desirable, but to the best of our knowledge no methods have been proposed for time-series data in the context of data squashing. In order to circumvent this problem, we propose DTWS (Dynamic Time Warping Squashed) tree, which employs a dissimilarity measure based on DTW, and compresses time sequences to the average time sequence vector. An average time sequence vector is obtained by a novel procedure which estimates correct shrinkage of a result of DTW. Experiments using the Australian sign language data demonstrate the superiority of the proposed method in terms of correctness of clustering, while its degradation of time efficiency is negligible.

Model-Based clustering for cross-sectional time series data

A model-based clustering method for cross-sectional time series data is proposed and applied to crop insurance programs. To design an effective grouprisk plan, an important step is to group together the farms that resemble each other and decide the number of clusters, both of which can be achieved via the model-based clustering. The mixture maximum likelihood is employed for inferences. However, with the presence of correlation and missing values, the exact maximum likelihood estimators (MLEs) are difficult to obtain. An approach for obtaining approximate MLEs is proposed and evaluated through simulation studies. A bootstrapping method is used to choose the number of components in the mixture model.