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

  • 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
  • Xiaoqian Wang, Yanfei Kang, Rob J Hyndman, Feng Li (2022) Distributed ARIMA Models for Ultra-long Time Series. International J Forecasting, to appear. Abstract DOI arXiv  code
  • Sevvandi Kandanaarachchi, Rob J Hyndman (2022) Leave-one-out kernel density estimates for outlier detection. J Computational & Graphical Statistics, 31(2), 586-599. Abstract DOI  pdf  code
  • Sayani Gupta, Rob J Hyndman, Dianne Cook and Antony Unwin (2022) Visualizing probability distributions across bivariate cyclic temporal granularities. J Computational & Graphical Statistics, 31(1), 14-25. Abstract DOI  pdf  code

Recent and upcoming seminars

  • 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...
  • Forecasting the future and the future of forecasting. (17 January 2022) More info...
  • Feasts & fables: modern tools for time series analysis. (17 November 2021) YouTube More info...