Clustering Time Series with Clipped Data
Machine Learning
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Time series clustering based on forecast densities
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
A review on time series data mining
Engineering Applications of Artificial Intelligence
| Hi-index | 12.05 |
Currently, there is an increased interest in time series clustering research, particularly for finding useful similar time series in various applied areas such as speech recognition, environmental research, finance and medical imaging. Clustering and classification of time series has the potential to analyze large volumes of data. Most of the traditional time series clustering and classification algorithms deal only with univariate time series data. In this paper, we develop an unsupervised learning algorithm for bivariate time series. The initial clusters are found using K-means algorithm and the model parameters are estimated using the EM algorithm. The learning algorithm is developed by utilizing component maximum likelihood and Bayesian Information Criteria (BIC). The performance of the developed algorithm is evaluated using real time data collected from a pollution centre. A comparative study of the proposed algorithm is made with the existing data mining algorithm that uses univariate autoregressive process of order 1 (AR(1)) model. It is observed that the proposed algorithm out performs the existing algorithms.