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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.16445 |
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Table of 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.