TBATS models

Forecasting in R

Rob J. Hyndman

Professor of Statistics at Monash University

TBATS model

  • Trigonometric terms for seasonality

  • Box-Cox transformations for heterogeneity

  • ARMA errors for short-term dynamics

  • Trend (possibly damped)

  • Seasonal (including multiple and non-integer periods)

Forecasting in R

US Gasoline data

gasoline %>% tbats() %>% forecast() %>%
  autoplot() +
  xlab("Year") + ylab("thousand barrels per day")

Screenshot 2017-05-02 15.32.43.png

Forecasting in R

Call center data

calls %>% window(start = 20) %>%
  tbats() %>% forecast() %>%
  autoplot() + xlab("Weeks") + ylab("Calls")

Screenshot 2017-05-02 15.35.20.png

Forecasting in R

TBATS model

  • Trigonometric terms for seasonality
  • Box-Cox transformations for heterogeneity
  • ARMA errors for short-term dynamics
  • Trend (possibly damped)
  • Seasonal (including multiple and non-integer periods)
  • Handles non-integer seasonality, multiple seasonal periods
  • Entirely automated
  • Prediction intervals often too wide
  • Very slow on long series
Forecasting in R

Let's practice!

Forecasting in R

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