{"title":"基于堆栈LSTM回归的曼谷地区pm2.5数据超参数优化","authors":"Voravarun Pattana-anake, Ferdin Joe John Joseph","doi":"10.1109/ICBIR54589.2022.9786465","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Hyper Parameter Optimization of Stack LSTM Based Regression for PM 2.5 Data in Bangkok\",\"authors\":\"Voravarun Pattana-anake, Ferdin Joe John Joseph\",\"doi\":\"10.1109/ICBIR54589.2022.9786465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":216904,\"journal\":{\"name\":\"2022 7th International Conference on Business and Industrial Research (ICBIR)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Business and Industrial Research (ICBIR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBIR54589.2022.9786465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Business and Industrial Research (ICBIR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBIR54589.2022.9786465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.