Corpus ID: 10806632

tsoutliers R Package for Detection of Outliers in Time Series

@inproceedings{LpezdeLacalle2016tsoutliersRP,
  title={tsoutliers R Package for Detection of Outliers in Time Series},
  author={Javier L{\'o}pez-de-Lacalle},
  year={2016}
}
  • Javier López-de-Lacalle
  • Published 2016
  • Computer Science
  • Time series data often undergo sudden changes that alter the dynamics of the data transitory or permanently. These changes are typically non-systematic and cannot be captured by standard time series models. That’s why they are known as exogenous or outlier effects. Detecting outliers is important because they have an impact on the selection of the model, the estimation of parameters and, consequently, on forecasts. An automatic procedure described in the literature to detect outliers in time… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 10 REFERENCES
    Time Series Analysis, Forecasting and Control.
    • 5,333
    Joint Estimation of Model Parameters and Outlier Effects in Time Series
    • 649
    • Highly Influential
    • PDF
    Time Series Analysis by State Space Methods
    • 1,438
    • PDF
    Introduction to time series and forecasting
    • 3,698
    Efficient tests for normality
    • 303
    Forecasting, Structural Time Series Models and the Kalman Filter.
    • 1,840
    Measuring Business Cycles in Economic Time Series
    • 36
    stsm: Structural Time Series Models, 2016
    • 2016