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Found 11 papers, 4 papers with code
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks.
Chronos: Learning the Language of Time Series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting.
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation
We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order.
Cross-Frequency Time Series Meta-Forecasting
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting.
Automatic Termination for Hyperparameter Optimization
Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time.
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
To democratize access to machine learning systems, it is essential to automate the tuning.
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
A Quantile-based Approach for Hyperparameter Transfer Learning
In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Despite its simplicity, we show that our approach considerably boosts BO by reducing the size of the search space, thus accelerating the optimization of a variety of black-box optimization problems.
A Copula approach for hyperparameter transfer learning
In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics.
