Can time series forecasting be automated? A benchmark and analysis
This work addresses the problem of method selection for practitioners in domains like finance and healthcare, but it is incremental as it focuses on benchmarking existing frameworks rather than introducing new forecasting techniques.
The research tackled the challenge of selecting suitable time series forecasting methods by proposing a comprehensive benchmark to evaluate and rank methods across diverse datasets, analyzing performance from frameworks like AutoGluon-Timeseries and sktime to inform decision-making for optimal predictions.
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.