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Found 6 papers, 3 papers with code
auto-sktime: Automated Time Series Forecasting
To meet the growing demand for efficient forecasting, we introduce auto-sktime, a novel framework for automated time series forecasting.
Automated Machine Learning for Remaining Useful Life Predictions
Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required.
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
It is thus of high interest to automate the forecasting process based on current market data.
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
Even though such automatically synthesized ML pipelines are able to achieve a competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines.
Incremental Search Space Construction for Machine Learning Pipeline Synthesis
We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.
Benchmark and Survey of Automated Machine Learning Frameworks
This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets.
