Detecting time series outliers

The tsoutliers() function in the forecast package for R is useful for identifying anomalies in a time series. However, it is not properly documented anywhere. This post is intended to fill that gap. The function began as an answer on CrossValidated and was later added to the forecast package because I thought it might be useful to other people. It has since been updated and made more reliable.

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Forecasting: Principles and Practice

Recent papers

  • Sayani Gupta, Rob J Hyndman, Dianne Cook (2021) Detecting distributional differences between temporal granularities for exploratory time series analysis. Abstract  pdf
  • Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman (2021) LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts. Abstract  pdf
  • Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis, Christoph Bergmeir (2021) Model selection in reconciling hierarchical time series. Machine Learning, to appear. Abstract  pdf
  • Sevvandi Kandanaarachchi, Rob J Hyndman (2021) Leave-one-out kernel density estimates for outlier detection. J Computational & Graphical Statistics, to appear. Abstract DOI  pdf  code
  • Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I Webb, Rob J Hyndman, Pablo Montero-Manso (2021) Monash Time Series Forecasting Archive. NeurIPS 2021 Datasets and Benchmarks. Abstract  pdf Online  code

Recent and upcoming seminars

  • Feasts & fables: modern tools for time series analysis. (17 November 2021) More info...
  • Uncertain futures: AAS2021. (3 November 2021) YouTube More info...
  • Feasts & fables: Time series analysis using R. (28 September 2021) More info...
  • The geometry of forecast reconciliation. (16 September 2021) More info...
  • GRATIS: GeneRAting TIme Series with diverse and controllable characteristics. (10 September 2021) YouTube More info...