Can time series forecasting be automated? A benchmark and analysis

• URL: http://arxiv.org/abs/2407.16445v2
• Date: Thu, 25 Jul 2024 17:53:38 GMT
• Title: Can time series forecasting be automated? A benchmark and analysis
• Authors: Anvitha Thirthapura Sreedhara, Joaquin Vanschoren,
• Abstract summary: Time series forecasting plays a pivotal role across various domains such as finance, healthcare, and weather. 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 proposes a comprehensive benchmark for evaluating and ranking time series forecasting methods across a wide range of datasets.
• Score: 4.19475889117731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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