Rob J. Hyndman

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We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition and the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be inadequate, and many of them are degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled(More)
We propose a new method for robust forecasting of age-specific mortality and fertility rates. To illustrate our methodology, we use annual Australian fertility rates (1921–2000) for five-year age groups (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49). The data were obtained from the Australian Bureau of Statistics and are shown as separate time series in(More)
We provide a new approach to automatic forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods provides forecasts that are equivalent to forecasts from a state space model. This equivalence allows: (1) easy calculation of the likelihood, the AIC and other model selection criteria;(More)
We consider bandwidth selection for the kernel estimator of conditional density with one explanatory variable. Several bandwidth selection methods are derived ranging from fast rules-of-thumb which assume the underlying densities are known to relatively slow procedures which use the bootstrap. The methods are compared and a practical bandwidth selection(More)
We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982–1985 and International Journal of Forecasting 1985–2005). During this period, over one third of all papers published in(More)
With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is critical for us to find a way to resolve the outstanding problems that make most clustering methods impractical under certain circumstances. When the time series is very long, some(More)
A challenge in phenology studies is understanding what constitutes phenological change amidst background variation. The majority of phenological studies have focussed on extracting critical points in the seasonal growth cycle, without exploiting the full temporal detail. The high degree of phenological variability between years demonstrates the necessity of(More)
Age-sex-specific population forecasts are derived through stochastic population renewal using forecasts of mortality, fertility and net migration. Functional data models with time series coefficients are used to model age-specific mortality and fertility rates. As detailed migration data are lacking, net migration by age and sex is estimated as the(More)
A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods,(More)