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Robin John Hyndman (born 2 May 1967 [citation needed]) is an Australian statistician known for his work on forecasting and time series. He is a Professor of Statistics at Monash University [ 1 ] and was Editor-in-Chief of the International Journal of Forecasting from 2005–2018. [ 2 ]
A First Course on Time Series Analysis – an open source book on time series analysis with SAS (Chapter 7) Box–Jenkins models in the Engineering Statistics Handbook of NIST; Box–Jenkins modelling by Rob J Hyndman; The Box–Jenkins methodology for time series models by Theresa Hoang Diem Ngo
[2] [3] According to Rob J. Hyndman, Editor-in-Chief of the International Journal of Forecasting (IJF), "The M3 data have continued to be used since 2000 for testing new time series forecasting methods. In fact, unless a proposed forecasting method is competitive against the original M3 participating methods, it is difficult to get published in ...
It was proposed in 2005 by statistician Rob J. Hyndman and Professor of Decision Sciences Anne B. Koehler, ... For a non-seasonal time series, [8] ...
Time series forecasting is the use of a model to predict future values based on previously observed ... Jan G.; Hyndman, Rob J. (2006). "25 Tears of Time Series ...
Spyros Makridakis (born 22 April 1941) is a professor at the University of Nicosia where he is the Director of the Institute for the Future (IFF) and an Emeritus Professor of Decision Sciences at INSEAD as well as the University of Piraeus and one of the world's leading experts on forecasting, with many journal articles and books on the subject.
In time series analysis used in statistics and econometrics, autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models are generalizations of the autoregressive moving average (ARMA) model to non-stationary series and periodic variation, respectively.
Seasonal adjustment or deseasonalization is a statistical method for removing the seasonal component of a time series.It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components.