计算机科学
时间序列
系列(地层学)
人工神经网络
人工智能
机器学习
机制(生物学)
集合(抽象数据类型)
均方预测误差
循环神经网络
深度学习
数据挖掘
古生物学
哲学
认识论
生物
程序设计语言
标识
DOI:10.1109/icicacs57338.2023.10099498
摘要
As a collection of time observations, time series has attracted extensive attention in artificial intelligence. Time series prediction is one of the important topics to obtain future trends. Therefore, based on the discussion of time series characteristics, temporal attention mechanism and deep learning time series prediction, this paper briefly discusses the open data set, experimental environment and parameter settings, and designs an improved time series PA-LSTM prediction model based on deep learning. Finally, through specific experimental analysis. The results show that the RMSLE and MAE values of the PA-LSTM prediction method designed in this paper are 0.012 and 0.010 respectively. The error is lower than other prediction methods. Therefore, the PA-LSTM prediction method has certain advantages.
科研通智能强力驱动
Strongly Powered by AbleSci AI