Can time series forecasting be automated? A benchmark and analysis
<|reference_start|>Can time series forecasting be automated? A benchmark and analysis: In the field of machine learning and artificial intelligence, time series forecasting plays a pivotal role across various domains such as finance, healthcare, and weather. However, the task of selecting the most suitable forecasting method for a given dataset is a complex task due to the diversity of data patterns and characteristics. This research aims to address this challenge by proposing a comprehensive benchmark for evaluating and ranking time series forecasting methods across a wide range of datasets. This study investigates the comparative performance of many methods from two prominent time series forecasting frameworks, AutoGluon-Timeseries, and sktime to shed light on their applicability in different real-world scenarios. This research contributes to the field of time series forecasting by providing a robust benchmarking methodology and facilitating informed decision-making when choosing forecasting methods for achieving optimal prediction.<|reference_end|>
@article{sreedhara2024can,
    title={Can time series forecasting be automated? A benchmark and analysis},
    author={Anvitha Thirthapura Sreedhara, Joaquin Vanschoren},
    journal={arXiv preprint arXiv:2407.16445},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16445},
    primaryClass={cs.LG}
}