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Aug, 2024
解锁LSTM在长期时间序列预测中的潜力
Unlocking the Power of LSTM for Long Term Time Series Forecasting
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Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren...
TL;DR
本研究解决了传统LSTM在时间序列预测中存在的短期记忆问题，并提出了一种新颖的算法P-sLSTM，结合了补丁和通道独立性，从而显著提升其性能。研究表明，P-sLSTM在时间序列预测任务中实现了最先进的成果，具有重要的应用潜力。
Abstract
Traditional recurrent neural network architectures, such as long short-term memory neural networks (
LSTM
), have historically held a prominent role in
Time Series Forecasting
(TSF) tasks. While the recently introd
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