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The Identification of Multiple Outliers in ARIMA Models

https://doi.org/10.1081/sta-120021331

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/for.3980070102openalex-snapshot

Outliers, level shifts, and variance changes in time series

inferredcites →https://doi.org/10.1016/s0304-4076(98)00036-0openalex-snapshot

Missing observations in ARIMA models: Skipping approach versus additive outlier approach

inferredcites →https://doi.org/10.1080/01621459.1986.10478253openalex-snapshot

Robust Estimates for ARMA Models

inferredcites →https://doi.org/10.1080/01621459.1993.10476364openalex-snapshot

Bayesian Inference and Prediction for Mean and Variance Shifts in Autoregressive Time Series

inferredcites →https://doi.org/10.1080/01621459.1993.10594321openalex-snapshot

Joint Estimation of Model Parameters and Outlier Effects in Time Series

inferredcites →https://doi.org/10.1080/01621459.1996.10476718openalex-snapshot

Modeling Flat Stretches, Bursts Outliers in Time Series Using Mixture Transition Distribution Models

inferredcites →https://doi.org/10.1080/07350015.1990.10509779openalex-snapshot

Random Level-Shift Time Series Models, ARIMA Approximations, and Level-Shift Detection

inferredcites →https://doi.org/10.1080/07350015.1990.10509795openalex-snapshot

Influential Observations in Time Series

inferredcites →https://doi.org/10.1080/07350015.1993.10509934openalex-snapshot

Detecting Level Shifts in Time Series

inferredcites →https://doi.org/10.1093/biomet/87.4.789openalex-snapshot

Outliers in multivariate time series

inferredcites →https://doi.org/10.1111/j.1467-9892.1994.tb00188.xopenalex-snapshot

BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER

inferredcites →https://doi.org/10.1111/j.2517-6161.1972.tb00912.xopenalex-snapshot

Outliers in Time Series

inferredcites →https://doi.org/10.1111/j.2517-6161.1989.tb01435.xopenalex-snapshot

Leave-K-Out Diagnostics for Time Series

inferredcites →https://doi.org/10.2307/1269010openalex-snapshot

Measuring Influence in Dynamic Regression Models

inferredcites →https://doi.org/10.2307/1270165openalex-snapshot

Estimation of Time Series Parameters in the Presence of Outliers

inferredcites →https://doi.org/10.2307/2291576openalex-snapshot

Modeling Flat Stretches, Bursts, and Outliers in Time Series Using Mixture Transition Distribution Models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The Impact of Outliers on Computing Conditional Risk Measures for Crude Oil and Natural Gas Commodity Futures Prices

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Outliers, Influential Observations, and Missing Data

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Handling Heterogeneity in Many Time Series

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Vector time series models

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An improved methodology for outlier detection in dynamic datasets

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A Unified Approach to Model Selection, Discrimination, Goodness of Fit and Outliers in Time Series

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Outlier Detection for Temporal Data

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Finding Outliers in Linear and Nonlinear Time Series

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Monitoring von Prognoseverfahren

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Bayesian methods for outliers detection in GNSS time series

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Comments on: Some recent theory for autoregressive count time series

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Robust correlation feature selection based support vector machine approach for high dimensional datasets

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Prediction of deterministic functions: an application of a Gaussian kriging model to a time series outlier problem

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Chapter 12 Forecasting with Breaks

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Bayesian inference in a multiple contaminated autoregressive model with trend

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Analysis of seasonal level shift (SLS) detection in SARIMA models

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Combining Bayesian method and Kalman smoother for detection additive outlier patches in autoregressive time series

inferred← citeshttps://doi.org/10.1080/03610918.2021.1887225openalex-snapshot

Appraisal of excess Kurtosis through outlier-modified GARCH-type models

inferred← citeshttps://doi.org/10.1080/03610920903259831openalex-snapshot

Innovational Outliers in INAR(1) Models

inferred← citeshttps://doi.org/10.1080/03610926.2015.1091082openalex-snapshot

The use of temporally aggregated data on detecting a mean change of a time series process

inferred← citeshttps://doi.org/10.1093/acprof:oso/9780199549498.003.0009openalex-snapshot

An Automatic Test of Super Exogeneity*

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A baseline time series data mining model for forecasts in port logistics and economics

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Understanding and using time series analyses in addiction research

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Influential observations in cointegrated VAR models: Danish money demand 1973–2003

inferred← citeshttps://doi.org/10.1111/jtsa.70010openalex-snapshot

Simultaneous Detection of Structural Breaks and Outliers in Time Series

inferred← citeshttps://doi.org/10.11648/j.ajtas.20160503.16openalex-snapshot

Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series

inferred← citeshttps://doi.org/10.11648/j.ajtas.20170604.14openalex-snapshot

Identification and Modeling of Outliers in a Discrete - Time Stochastic Series

inferred← citeshttps://doi.org/10.1198/016214505000001131openalex-snapshot

Outlier Detection in Multivariate Time Series by Projection Pursuit

inferred← citeshttps://doi.org/10.1201/b11823-19openalex-snapshot

Additive Outlier Detection in Seasonal ARIMA Models by a Modified Bayesian Information Criterion

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Strengthening Disaster Response Through Analysis of Crime During Hurricane Ike

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Data cleaning in the process industries

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Odporne wielopopulacyjne modelowanie oraz prognozowanie umieralności

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Modeling the Effects of Outliers on the Estimation of Linear Stochastic Time Series Model

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Application of Iterative Approaches in Modeling the Efficiency of ARIMA-GARCH Processes in the Presence of Outliers

inferred← citeshttps://doi.org/10.48550/arxiv.0903.2421openalex-snapshot

Outliers in INAR(1) models

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Heteroscedasticity Detection in the existence of Outliers in Discrete-Time Series

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An Improved Iterative Procedure for Outlier Detection in Time Series

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