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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| Main Author: | |
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/87887 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |