Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.16445 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911967200935936 |
|---|---|
| author | Sreedhara, Anvitha Thirthapura Vanschoren, Joaquin |
| author_facet | Sreedhara, Anvitha Thirthapura Vanschoren, Joaquin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can time series forecasting be automated? A benchmark and analysis Sreedhara, Anvitha Thirthapura Vanschoren, Joaquin Machine Learning 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. |
| title | Can time series forecasting be automated? A benchmark and analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.16445 |