The cricketdata package

Four functions The cricketdata package has been around for a few years on github, and it has been on CRAN since February 2022. There are only four functions: fetch_cricinfo(): Fetch team data on international cricket matches provided by ESPNCricinfo. fetch_player_data(): Fetch individual player data on international cricket matches provided by ESPNCricinfo. find_player_id(): Search for the player ID on ESPNCricinfo. fetch_cricsheet(): Fetch ball-by-ball, match and player data from Cricsheet. Jacquie Tran wrote the first version of the fetch_cricsheet() function, and the vignette which demonstrates it.

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Monash time series forecasting repository

The Monash time series forecasting respository is a comprehensive collection of time series data made available in a convenient form to encourage empirical forecast evaluations. The repository includes the data from many forecasting competitions including the M1, M3, M4, NN5, tourism, and KDD cup 2018, as well as many other data sets from diverse applications. The associated paper discusses the various data sets and their characteristics. Where a time series collection contains data with different observation frequencies, they are split into different data sets so that the series within each data set has the same frequency.

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Simulating from TBATS models

I’ve had several requests for an R function to simulate future values from a TBATS model. We will eventually include TBATS in the fable package, and the facilities will be added there. But in the meantime, if you are using the forecast package and want to simulate from a fitted TBATS model, here is how do it. Simulating via one-step forecasts Doing it efficiently would require a more complicated approach, but this is super easy if you are willing to sacrifice some speed.

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Recent papers

  • Anastasios Panagiotelis, Puwasala Gamakumara, George Athanasopoulos and Rob J Hyndman (2022) Probabilistic forecast reconciliation: properties, evaluation and score optimisation. European J Operational Research, to appear. Abstract DOI  pdf  code
  • Bahman Rostami-Tabar, Mohammad M Ali, Tao Hong, Rob J Hyndman, Michael D Porter, Aris Syntetos (2022) Forecasting for Social Good. International Journal of Forecasting, 38(3), 1245-1257. Abstract DOI  pdf
  • Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman (2022) LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts. International J Forecasting, to appear. Abstract arXiv
  • Kasun Bandara, Rob J Hyndman, Christoph Bergmeir (2022) MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns. International J Operational Research, to appear. Abstract arXiv
  • Xiaoqian Wang, Rob J Hyndman, Feng Li, Yanfei Kang (2022) Forecast combinations: an over 50-year review. Abstract arXiv  code

Recent and upcoming seminars

  • Decomposing time series with complex seasonality. (23 August 2022) More info...
  • Creating social good for forecasters. (10 July 2022) More info...
  • Developing good research habits. (22 March 2022) More info...
  • Forecasting the old-age dependency ratio to determine a sustainable pension age. (24 February 2022) More info...
  • Feature-based time series analysis. (2 February 2022) More info...