Rob J. Hyndman's Blog, page 3

Time series and forecasting workshop: 9-10 November 2022

Migrating from Disqus to giscus

Migrating to Quarto

Probabilistic forecast reconciliation: properties, evaluation and score optimisation

We develop a framework for forecasting multivariate data that follow known linear constraints. This is particularly common in forecasting where some variables are aggregates of others, commonly referred to as hierarchical time series, but also arises in other prediction settings. For point forecasting, an increasingly popular technique is reconciliation, whereby forecasts are made for all series (so-called base forecasts) and subsequently adjusted to cohere with the constraints. We extend reconciliation from point forecasting to probabilistic forecasting.

Decomposing time series with complex seasonality

COMPSTAT 2022, Bologna, Italy, 23-26 August 2022. Time series data often contain a rich complexity of seasonal patterns. Time series that are observed at a sub-daily level can exhibit multiple seasonal patterns corresponding to different granularities such as hour-of-the-day, day-of-the-week or month-of-the-year. They can be nested (e.g., hour-of-the-day within day-of-the-week) and non-nested (e.g., day-of-the-year in both the Gregorian and Hijri calendars). We will discuss two new time series decomposition tools for handling seasonalities in time series data: MSTL and STR.

Creating social good for forecasters

Forecasting for Social Good Workshop, Oxford UK, 10 July 2022 Social good is created whenever we make new forecasting methods and resources freely available and usable. That could take the form of open source software and data, open access papers and textbooks, reproducible source files, and so on. I will discuss progress in this area over the last 25 years, and reflect on my own experiences in publishing forecasting papers, books and software.

Forecasting for Social Good

Forecasting plays a critical role in the development of organisational business strategies. Despite a considerable body of research in the area of forecasting, the focus has largely been on the financial and economic outcomes of the forecasting process as opposed to societal benefits. Our motivation in this study is to promote the latter, with a view to using the forecasting process to advance social and environmental objectives such as equality, social justice and sustainability.

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.

The cricketdata package

Forecast combinations: an over 50-year review

Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities. Combining multiple forecasts produced from the single (target) series is now widely used to improve accuracy through the integration of information gleaned from different sources, thereby mitigating the risk of identifying a single ``best'' forecast. Combination schemes have evolved from simple combination methods without estimation, to sophisticated methods involving time-varying weights, nonlinear combinations, correlations among components, and cross-learning.