LGNov 22, 2021
Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping
arXiv:2111.11848v1
Originality Synthesis-oriented
AI Analysis
It provides a synthesis for researchers in environmental monitoring, but is incremental as it compiles existing literature without new findings.
This systematic mapping study reviewed the use of LSTM networks for air quality time series prediction, identifying gaps and potential approaches for future research without reporting specific numerical results.
This systematic mapping study investigates the use of Long short-term memory networks to predict time series data about air quality, trying to understand the reasons, characteristics and methods available in the scientific literature, identify gaps in the researched area and potential approaches that can be exploited on later studies.