A Hybrid Framework Integrating Traditional Models and Deep Learning for Multi-Scale Time Series Forecasting

A Hybrid Framework Integrating Traditional Models and Deep Learning for Multi-Scale Time Series Forecasting

Time series forecasting is critical for decision-making in numerous domains, yet achieving high accuracy across both short-term and long-term horizons remains challenging. In this paper, we propose a general hybrid forecasting framework that integrates a traditional statistical model (ARIMA) with mo...

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Bibliographic Details
Main Authors: Zihan Liu, Zijia Zhang, Weizhe Zhang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Entropy
Subjects:
hybrid time series forecasting
multi-scale prediction mechanism
statistical–deep learning integration
Online Access:https://www.mdpi.com/1099-4300/27/7/695
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