Speaker
Description
mlr3forecast extends the mlr3 ecosystem to support time series forecasting workflows. It introduces a dedicated forecasting task class and resampling strategies that respect temporal ordering, enabling forecasting models to be benchmarked, tuned, and combined in a systematic way. Learners wrapping established forecasting methods such as ARIMA and ETS can be used alongside any mlr3 learner adapted to forecasting tasks, enabling hyperparameter tuning with mlr3tuning, preprocessing pipelines with mlr3pipelines, and benchmarking within a single consistent interface. In this talk, we will give an overview of mlr3forecast's design and key features and demonstrate its application in practical forecasting and benchmarking scenarios. https://github.com/mlr-org/mlr3forecast
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | time series, machine learning, package development, mlr3 |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |