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Entropy-based fuzzy clustering of interval-valued time series
This paper proposes a fuzzy C -medoids-based clustering method with entropy regularization to solve the issue of grou** complex data as...
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MLE for the parameters of bivariate interval-valued model
With contemporary data sets becoming too large to analyze the data directly, various forms of aggregated data are becoming common. The original...
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Monitoring photochemical pollutants based on symbolic interval-valued data analysis
This study considers monitoring photochemical pollutants for anomaly detection based on symbolic interval-valued data analysis. For this task, we...
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Symbolic interval-valued data analysis for time series based on auto-interval-regressive models
This study considers interval-valued time series data. To characterize such data, we propose an auto-interval-regressive (AIR) model using the order...
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Supervised dimension reduction for functional time series
Functional time series model has been the subject of the most research in recent years, and since functional data is infinite dimensional, dimension...
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Dimension reduction and visualization of multiple time series data: a symbolic data analysis approach
Exploratory analysis and visualization of multiple time series data are essential for discovering the underlying dynamics of a series before...
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Partition of Interval-Valued Observations Using Regression
Both regression modeling and clustering methodologies have been extensively studied as separate techniques. There has been some activity in using...
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Causality in extremes of time series
Consider two stationary time series with heavy-tailed marginal distributions. We aim to detect whether they have a causal relation, that is, if a...
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LRD spectral analysis of multifractional functional time series on manifolds
This paper addresses the estimation of the second-order structure of a manifold cross-time random field (RF) displaying spatially varying Long Range...
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A class of transformed joint quantile time series models with applications to health studies
Extensions of quantile regression modeling for time series analysis are extensively employed in medical and health studies. This study introduces a...
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Modeling and inferences for bounded multivariate time series of counts
This paper considers modeling bounded multivariate time series of counts and the inferential procedures of this model. For modeling, we introduce a...
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Zero-modified count time series modeling with an application to influenza cases
The past few decades have seen considerable interest in modeling time series of counts, with applications in many domains. Classical and Bayesian...
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Nonstatistical Methods for Analysis, Forecasting, and Mining Time Series
This is an overview paper, in which we briefly present results obtained over several years in the analysis, forecasting, and mining information from...
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An enhanced version of the SSA-HJ-biplot for time series with complex structure
HJ-biplots can be used with singular spectral analysis to visualize and identify patterns in univariate time series. Named SSA-HJ-biplots, these...
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Point and probabilistic forecast reconciliation for general linearly constrained multiple time series
Forecast reconciliation is the post-forecasting process aimed to revise a set of incoherent base forecasts into coherent forecasts in line with given...
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Time Series II
This chapter continues the empirical analysis of the central England daily temperature series using Fourier techniques indexed by frequencies. The...
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Information Theoretic Results for Stationary Time Series and the Gaussian-Generalized von Mises Time Series
This chapter presents some novel information theoretic results for the analysis of stationary time series in frequency domain. In particular, the...
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Time Series and Stationary Processes
This chapter introduces basic concepts such as time series, stationary process and covariance function. Subsequently, the time domain of a stationary...
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Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series
We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated...
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Solving an Inverse Problem for Time-Series-Valued Computer Simulators via Multiple Contour Estimation
Computer simulators are often used as a substitute of complex real-life phenomena, which are either expensive or infeasible to experiment with. This...
