Accurate land cover information is required for science, monitoring, and reporting. Land
cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring …

The beginning of the age of artificial intelligence and machine learning has created new
challenges and opportunities for data analysts, statisticians, mathematicians …

The statistical discrimination and clustering literature has studied the problem of identifying
similarities in time series data. Some studies use non-parametric approaches for splitting a …

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 …

Multivariate time series (MTS) data are widely used in a very broad range of fields, including
medicine, finance, multimedia and engineering. In this paper a new approach for MTS …

In this paper we propose different robust fuzzy clustering models for classifying
heteroskedastic (volatility) time series, following the so-called model-based approach to time …

Clustering of multivariate time series is a central problem in data mining with applications in
many fields. Frequently, the clustering target is to identify groups of series generated by the …

时间序列聚类可以根据相似性将对象集分为不同的组, 从而反映出同组对象的相似性特征和不同
组对象之间的差异特征. 当序列维度较高时, 传统的时间序列聚类方法容易受噪声影响 …

In this study, regional climates of Iran were identified based on the properties of the monthly
rainfall time series models of 28 main cities of Iran. The autocorrelation (ACF) and partial …

In this paper, following the Partitioning Around Medoids (PAM) approach and the fuzzy
theory, we propose a clustering model for financial time series based on the estimated …