Can time series forecasting be automated? A benchmark and analysis

TL;DR

This paper presents a comprehensive benchmark for evaluating various time series forecasting methods across diverse datasets, aiming to guide practitioners in selecting the most suitable approach for different real-world scenarios.

Contribution

It introduces a robust benchmarking methodology and compares multiple forecasting frameworks, AutoGluon-Timeseries and sktime, to enhance decision-making in time series prediction.

Findings

AutoGluon-Timeseries outperforms other methods on several datasets.

Different methods excel depending on data characteristics.

Benchmarking aids in selecting optimal forecasting techniques.

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.