Forecasts are always wrong

Recently I was interviewed for the Monash Business School podcast “Thought Capital” on the topic of forecasting. You can listen to the episode here (or read the transcript).

Non-Gaussian forecasting using fable

library(tidyverse) library(tsibble) library(lubridate) library(feasts) library(fable) In my previous post about the new fable package, we saw how fable can produce forecast distributions, not just point forecasts. All my examples used Gaussian (normal) distributions, so in this post I want to show how non-Gaussian forecasting can be done. As an example, we will use eating-out expenditure in my home state of Victoria. vic_cafe <- tsibbledata::aus_retail %>% filter( State == "Victoria", Industry == "Cafes, restaurants and catering services" ) %>% select(Month, Turnover) vic_cafe %>% autoplot(Turnover) + ggtitle("Monthly turnover of Victorian cafes") Forecasting with transformations Clearly the variance is increasing with the level of the series, so we will consider modelling a Box-Cox transformation of the data.

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Tidy forecasting in R

The fable package for doing tidy forecasting in R is now on CRAN. Like tsibble and feasts, it is also part of the tidyverts family of packages for analysing, modelling and forecasting many related time series (stored as tsibbles). For a brief introduction to tsibbles, see this post from last month. Here we will forecast Australian tourism data by state/region and purpose. This data is stored in the tourism tsibble where Trips contains domestic visitor nights in thousands.

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Recent publications

  • Anastasios Panagiotelis, Puwasala Gamakumara, George Athanasopoulos, Rob J Hyndman (2019) Forecast reconciliation: A geometric view with new insights on bias correction. Abstract  pdf
  • Priyanga Dilini Talagala, Rob J Hyndman, Catherine Leigh, Kerrie Mengersen and Kate Smith-Miles (2019) A feature-based framework for detecting technical outliers in water-quality data from in situ sensors. Water Resources Research, to appear. Abstract  pdf
  • Catherine Leigh, Sevvandi Kandanaarachchi, James M McGree, Rob J Hyndman, Omar Alsibai, Kerrie Mengersen, Erin E Peterson (2019) Predicting sediment and nutrient concentrations in rivers using high-frequency water quality surrogates. PLOS ONE, to appear. Abstract  pdf
  • Sevvandi Kandanaarachchi, Rob J Hyndman (2019) Dimension reduction for outlier detection using DOBIN. Abstract  pdf

Recent and upcoming seminars

  • Tidy time series analysis in R. (27 September 2019) More info...
  • Feature-based time series analysis. (27 September 2019) More info...
  • Tidy time series analysis in R. (26 September 2019) More info...
  • Forecasting is not prophecy: dealing with high-dimensional probabilistic forecasts in practice. (21 August 2019) More info...