Advice to PhD applicants

For students who are interested in doing a PhD at Monash under my supervision. First, check that you satisfy the following criteria: You must have completed a degree in statistics that involved some research component (e.g., an honours or masters thesis). A degree in computer science, mathematics or econometrics might be acceptable if it contained a substantial amount of statistics. A degree in any other field is not sufficient background to work with me.

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forecast 8.5

The latest minor release of the forecast package has now been approved on CRAN and should be available in the next day or so. Version 8.5 contains the following new features Updated tsCV() to handle exogenous regressors. Reimplemented naive(), snaive(), rwf() for substantial speed improvements. Added support for passing arguments to auto.arima() unit root tests. Improved auto.arima() stepwise search algorithm (some neighbouring models were missed previously). We haven’t done a major release for two years, and there is unlikely to be another one now.

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Recent publications

  • George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J Hyndman and Mohamed Affan (2019) Hierarchical forecasting. Macroeconomic forecasting in the age of big data, ed. P. Fuleky, Chapter 23. Abstract  pdf
  • Earo Wang, Di Cook and Rob J Hyndman (2019) A new tidy data structure to support exploration and modeling of temporal data. Abstract  pdf
  • Priyanga Dilini Talagala, Rob J Hyndman, Catherine Leigh, Kerrie Mengersen and Kate Smith-Miles (2019) A feature-based framework for detecting technical outliers in water-quality data from in situ sensors. Abstract  pdf
  • Catherine Leigh, Omar Alsibai, Rob J Hyndman, Sevvandi Kandanaarachchi, Olivia C King, James M McGree, Catherine Neelamraju, Jennifer Strauss, Priyanga Dilini Talagala, Ryan S Turner, Kerrie Mengersen, Erin E Peterson (2019) A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. Science of the Total Environment, to appear.. Abstract DOI  pdf

Recent and upcoming seminars

  • High-dimensional time series analysis. (17 August 2019) More info...
  • Feature-based forecasting algorithms for large collections of time series. (25 January 2019) More info...
  • Data visualization for functional time series. (11 December 2018) More info...
  • Seasonal functional autoregressive models. (9 December 2018) More info...