Earo Wang

I got my B.Comm.(Hons) from Department of Econometrics and Business Statistics, Monash University, Australia. My honours thesis focused on Exploratory visualisation of big time series data, supervised by Rob J Hyndman. My research interests lie in data visualisation, computational statistics and time series analysis.

Wanna talk? Please drop me a line to earo.wang(at)gmail.com


Research Assistant

Nov 2013 – Present | Monash University

  • Anomalous time series detection. I’ve proposed several statistical metrics to measure the “anomalousness” of time series and implemented the methods of outlier detection in the anomalous package for R.
  • R packages development on forecasting. I’ve rewritten the hts package in order to make it more efficient and flexible. I’ve also contributed some new functions to the forecast and demography packages.

Teaching Associate

Jul 2014 – Jun 2015 | Monash University

  • ETC1000 - Business and economic statistics - 2014 S2, 2015 S1.

Visualisation of big time series data

We examine some recently developed techniques for visualising multiple time series and propose some simple and quick methods for graphical representation of estimates of trend, seasonality and remainders from a time series. Furthermore, we also propose an approach to visualisation of very large collections of time series using some dimension reduction techniques. First, we compute a vector of statistical metrics describing a range of characteristics of each time series, and then use Principal Component Analysis to further reduce the data to two dimensions. The biplot, based on the first two principal components, provides a useful exploratory tool, and allows the identification of anomalous time series. We order the time series according to their data density and data depth in the space of the first two principal components. The method enables the most unusual series, based on their feature vectors, to be identified.

Hierarchical forecasting

The R package hts presents functions to create, plot and forecast hierarchical and grouped time series. In forecasting hierarchical and grouped time series, the base methods implemented include ETS, ARIMA and the naive (random walk) models. Forecasts for grouped time series are calibrated using bottom-up and optimal combination methods. Forecasts for hierarchical time series are distributed in the hierarchy using bottom-up, top-down, middle-out and optimal combination methods. Three top-down methods are available: the two Gross-Sohl methods and the forecast-proportion approach of Hyndman, Ahmed, and Athanasopoulos (2011).