Contraceptive forecasting competition

Here’s an interesting new forecasting competition that came via my inbox this week. Contraceptive access is vital to safe motherhood, healthy families, and prosperous communities. Greater access to contraceptives enables couples and individuals to determine whether, when, and how often to have children. In low- and middle-income countries (LMIC) around the world, health systems are often unable to accurately predict the quantity of contraceptives necessary for each health service delivery site, in part due to insufficient data, limited staff capacity, and inadequate systems.

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Terminology matters

I was reminded again this week that getting the right terminology is important. Some of my colleagues who work in machine learning wrote a paper entitled “Time series regression” which began with “This paper introduces Time Series Regression (TSR): a little-studied task …”. Statisticians and econometricians have done time series regression for many decades, so this beginning led to the paper being lampooned on Twitter. The problem arose due to clashes in terminology being used in different fields.

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Seasonal mortality rates

The weekly mortality data recently published by the Human Mortality Database can be used to explore seasonality in mortality rates. Mortality rates are known to be seasonal due to temperatures and other weather-related effects (Healy 2003).

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Recent publications

  • Sayani Gupta, Rob J Hyndman, Dianne Cook and Antony Unwin (2020) Visualizing probability distributions across bivariate cyclic temporal granularities. Abstract  pdf
  • Bahman Rostami-Tabar, Mohammad M Ali, Tao Hong, Rob J Hyndman, Michael D Porter, Aris Syntetos (2020) Forecasting for Social Good. Abstract  pdf
  • Rob J Hyndman, Yijun Zeng, Han Lin Shang (2020) Forecasting the old-age dependency ratio to determine a sustainable pension age. Abstract  pdf
  • Pablo Montero-Manso, Rob J Hyndman (2020) Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality. Abstract  pdf
  • Rob J Hyndman (2020) Quantile forecasting with ensembles and combinations. Chapter in Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning, eds. Gilliland, Tashman & Sglavo. John Wiley & Sons. Abstract  pdf

Recent and upcoming seminars

  • The geometry of forecast reconciliation. (28 August 2020) More info...
  • Ensemble forecasts using fable. (14 August 2020) More info...
  • Podcast episode: the curious quant. (19 July 2020) More info...
  • Forecasting the Future & the Future of Forecasting. (27 May 2020) More info...
  • Developing good research habits. (31 March 2020) More info...