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Rob J. Hyndman

Forecasting:Principles & PracticeLeader: Rob J Hyndman23-25 September 2014 University of Western Introduction to Introduction .. Some case studies .. Time series data .. Some simple forecasting methods .. Lab Session 1 .. 132 The forecaster s Time series graphics .. Seasonal or cyclic? .. Autocorrelation .. Forecast residuals .. White noise .. Evaluating forecast accuracy .. Lab Session 2 .. 323 Exponential The state space perspective .. Simple exponential smoothing .. Trend methods .. Seasonal methods .. Lab Session 3 .. Taxonomy of exponential smoothing methods .. Innovations state space models .. ETS in R .. Forecasting with ETS models.

•Electricity demand •Ageing population •Fertilizer sales Poll: How experienced are you in forecasting? 1. Guru: I wrote the book, done it for decades, now I do the conference circuit. 2.Expert: It has been my full time job for more than a decade. 3.Skilled: I have been doing it for years. 4.Comfortable: I understand it and have done it.

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Forecasting time series using R - Rob J Hyndman

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Forecasting time series using R Time series in R 2 Outline 1 Time series in R 2 Some simple forecasting methods 3 Measuring forecast accuracy 4 Exponential smoothing 5 Box-Cox transformations 6 ARIMA forecasting 7 Difficult seasonality 8 forecast() function 9 Time series cross-validation

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