A Large-Scale Empirical Study of Aligned Time Series Forecasting

A Large-Scale Empirical Study of Aligned Time Series Forecasting

Automated Machine Learning (AutoML) tools for time series forecasting represent a frontier in both academic and industrial research, addressing the need for efficient, accurate predictions in various domains. This study focuses on the development of Automated Time Series Forecasting (AutoTS), specif...

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Bibliographic Details
Main Authors: Polina Pilyugina, Svetlana Medvedeva, Kirill Mosievich, Ilya Trofimov, Alina Kostromina, Dmitry Simakov, Evgeny Burnaev
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Time series forecasting
meta-learning
portfolio
model selection
meta-feature selection
Online Access:https://ieeexplore.ieee.org/document/10677532/
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