auto-sktime: Automated Time Series Forecasting

arXiv:2312.08528v3 Announce Type: replace
Abstract: 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 …

abstract arxiv auto automated challenges cs.lg data data-driven decision demand diverse forecasting landscape making novel pivotal series time series time series forecasting type