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

In this paper, we propose new methods for time series classification and clustering. These
methods are based on techniques of Topological Data Analysis (TDA) such as persistent …

Traditional and fuzzy cluster analyses are applicable to variables whose values are
uncorrelated. Hence, in order to cluster time series data which are usually serially …

We investigate the fuzzy clustering of interval time series using wavelet variances and
covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional …

In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy
subjects and those of individuals with specific heart conditions. We propose an approach for …

Crop type identification is the initial stage and an important part of the agricultural monitoring
system. It is well known that synthetic aperture radar (SAR) Sentinel-1A imagery provides a …

The literature on time-series clustering methods has increased considerably over the last
two decades with a wide range of applications in many different fields, including geology …

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of
stock returns from high‐frequency financial data. The proposed hybrid model combines the …

Traditional procedures for clustering time series are based mostly on crisp hierarchical or
partitioning methods. Given that the dynamics of a time series may change over time, a time …

Background Electroencephalogram (EEG) signals record electrical activity on the scalp.
Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted …