auto-sktime: Automated Time Series Forecasting
CoRR(2023)
摘要
In today's data-driven landscape, time series forecasting is pivotal in
decision-making across various sectors. Yet, the proliferation of more diverse
time series data, coupled with the expanding landscape of available forecasting
methods, poses significant challenges for forecasters. To meet the growing
demand for efficient forecasting, we introduce auto-sktime, a novel framework
for automated time series forecasting. The proposed framework uses the power of
automated machine learning (AutoML) techniques to automate the creation of the
entire forecasting pipeline. The framework employs Bayesian optimization, to
automatically construct pipelines from statistical, machine learning (ML) and
deep neural network (DNN) models. Furthermore, we propose three essential
improvements to adapt AutoML to time series data: First, pipeline templates to
account for the different supported forecasting models. Second, a novel
warm-starting technique to start the optimization from prior optimization runs.
Third, we adapt multi-fidelity optimizations to make them applicable to a
search space containing statistical, ML and DNN models. Experimental results on
64 diverse real-world time series datasets demonstrate the effectiveness and
efficiency of the framework, outperforming traditional methods while requiring
minimal human involvement.
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