Modeling Interval Time Series with Space–Time Processes

DOI10.1080/03610926.2013.782200zbMath1342.37076OpenAlexW2156963701WikidataQ57675542 ScholiaQ57675542MaRDI QIDQ3458101

Paula Brito, Paulo Teles

Publication date: 8 December 2015

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.782200

zbMATH Keywords

predictioninterval datainterval time seriesspacetime AR model

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Meteorology and atmospheric physics (86A10) Time series analysis of dynamical systems (37M10)

Related Items (14)

Model averaging for interval-valued data ⋮ Bootstrap based multi-step ahead joint forecast densities for financial interval-valued time series ⋮ Brexit and its impact on the US stock market ⋮ Symbolic interval-valued data analysis for time series based on auto-interval-regressive models ⋮ Exploratory data analysis for interval compositional data ⋮ A test to compare interval time series ⋮ Analysis of dependent data aggregated into intervals ⋮ Threshold autoregressive models for interval-valued time series data ⋮ Far beyond the classical data models: symbolic data analysis ⋮ Clustering of interval time series ⋮ Uncertainty shocks of Trump election in an interval model of stock market ⋮ Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models ⋮ Association measures for interval variables ⋮ Wavelet-based fuzzy clustering of interval time series

Uses Software

• SODAS

Cites Work

• Interval time series analysis with an application to the sterling-dollar exchange rate
• Linear disriminant analysis for interval data
• Constrained linear regression models for symbolic interval-valued variables
• Centre and range method for fitting a linear regression model to symbolic interval data
• Generalized autoregressive conditional heteroscedasticity
• Statistical analysis of environmental space-time processes.
• Principal Component Analysis for Non-Precise Data
• Symbolic Data Analysis
• On Hypotheses Testing for the Selection of Spatio-Temporal Models
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Model-Based Geostatistics
• Introduction to Time Series and Forecasting
• Applied Time Series Econometrics
• Statistics for Spatial Data
• Symbolic Data Analysis and the SODAS Software
• Analysis of Financial Time Series
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data

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