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Functional linear non-Gaussian acyclic model for causal discovery
In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a linear non-Gaussian acyclic model (LiNGAM),...
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Non-linear INAR(1) processes under an alternative geometric thinning operator
We propose a novel class of first-order integer-valued AutoRegressive (INAR(1)) models based on a new operator, the so-called geometric thinning...
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Time Series
A time series is a collection of data points ordered chronologically and recorded at successive time intervals. These data points can be taken over...
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Some Non-linear AR-type Models for Non-Gaussian Time Series
: The sequences of non-negative rvs find applications in many areas of the real world. For example, sequence of times to events in survival analysis,...
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Time Series
Time series refers to any group of statistical information accumulated at regular intervals. It is a quantitative method used to determine patterns...
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Predictive Root Based Bootstrap Prediction Intervals in Neural Network Models for Time Series Forecasting
Time series (TS) modelling is an important area in the domain of statistics, as it enables us to comprehend the dynamics underlying a particular...
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Linear Time Series Models with Non-Gaussian Innovations
The time series models with normally distributed innovations generate stationary normal sequences. However, if the innovations are not normal then...
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Quadratic Prediction of Time Series via Auto-Cumulants
Nonlinear prediction of time series can offer potential accuracy gains over linear methods when the process is nonlinear. As there are numerous...
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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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Non-Linear and Non-Gaussian State Space Models
This chapter discusses estimation for non-linear and non-Gaussian state space methods. We start by defining conditionally Gaussian and more general...
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Z-Process Method for Change Point Problems in Time Series
Z-process method was introduced as a general unified approach based on partial estimation functions to construct a statistical test in change point...
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Correlation Integral for Stationary Gaussian Time Series
The correlation integral of a time series is a normalized coefficient that represents the number of close pairs of points of the series lying in...
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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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Least-Squares Wavelet Analysis of Rainfalls and Landslide Displacement Time Series Derived by PS-InSAR
Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) data is a crucial step for monitoring the displacement of the Earth’s...
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Non-Gaussian Autoregressive-Type Time Series
This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of...
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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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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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Information Extraction: Non-time Series Methods
I focused on time series data for predicting an outcome in Chaps. 3 and 4...
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Estimating weak periodic vector autoregressive time series
This article develops the asymptotic distribution of the least squares estimator of the model parameters in periodic vector autoregressive time...
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Time Series Analysis and Prediction
In this chapter, we present essential parts of time series analysis, with the objective of predicting or forecasting its future development....