Time series data have grown at an explosive rate in numerous domains and stimulated a surge of time modeling research. A comprehensive comparison different models, for considered analytics task, provides useful guidance on model selection practitioners. Data scarcity is universal issue that occurs vast range problems, due to the high costs associated with collecting, generating, labeling as wel...

In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, perform binary and ternary two public datasets that consist of physiological signals collected under stress non-stress conditions. We accomplish our goal by using persistent homology to engineer stable features after use a delay embedding the subwindowing instead wi...

There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling...

This study examines the application of Chi-Square test in time series data which considers mixed model structure and linear trending curve. The is applied to seasonal variances Buys-Ballot table. emphasis assess validity using empirical examples. One hundred simulated numerical examples are used illustrate applicability test. Using example, successfully recorded 100% times for model. expresses ...

We develop time series analysis of functional data, treating the whole curve as a random realization from a distribution on functions that evolve over time. The method consists of principal components analysis of functional data and subsequently modelling the principal component scores as vector ARMA process. We carry out the estimation of VARMA parameters using the equivalent state space repre...