AIR QUALITY FORECASTING ON TIME SERIES DATA WITH ANOMALIES USING THE LSTM-XGBOOST APPROACH

AIR QUALITY FORECASTING ON TIME SERIES DATA WITH ANOMALIES USING THE LSTM-XGBOOST APPROACH

Air pollution is a global issue that significantly impacts human health and the environment. The COVID-19 pandemic introduced unexpected changes in air quality patterns, necessitating an approach capable of handling data pattern shifts and anomalies. This study aims to demonstrate that LSTM-XGBoo...

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Bibliographic Details
Main Author:	Layalia S.A.G., Aurell
Format:	Theses
Language:	Indonesia
Online Access:	https://digilib.itb.ac.id/gdl/view/87887
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Institut Teknologi Bandung
Language:	Indonesia

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