基于堆栈LSTM回归的曼谷地区pm2.5数据超参数优化

Voravarun Pattana-anake, Ferdin Joe John Joseph
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引用次数: 6

摘要

2.5微米量级的颗粒物污染引起了世界上人口最稠密的城市的关注。多年来,对预测分析进行了各种各样的研究。深度学习作为一种新技术正在改变解决分类和回归问题的面貌。过去提出了各种基于长短期记忆的结构来预测时间序列数据。对激活函数和优化函数进行随机化,选择最佳组合。在PM2.5数据上使用这种选择配置的堆栈LSTM优于现有的基于LSTM的架构。在LSTM层中加入Adamax优化器并对激活函数进行微调,获得了更好的性能。本文报告的性能指标足够明显,表明通过层随机化获得的优化超参数所提出的体系结构具有较小的错误率和训练损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyper Parameter Optimization of Stack LSTM Based Regression for PM 2.5 Data in Bangkok
Particulate Matter pollution with the magnitude of 2.5 microns is raising concerns from the most thickly populated cities around the world. There are various studies conducted on predictive analytics over the years. Deep learning has emerged as a new technology which is transforming the face of solving classification and regression problems. Various Long Short Term Memory based architectures are proposed in the past to predict time series data. Randomization of activation and optimization functions was done and the best performing combination is selected. Stack LSTM with this selected configuration on the PM2.5 data is found to be better than the existing LSTM based architectures. Inclusion of Adamax optimizer and fine tuning the activation functions in the LSTM layers gave better performance. The performance metrics reported in this paper are evident enough that the proposed architecture with optimized hyperparameters obtained by randomization of layers is found to perform with lesser error rates and training loss.
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