Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping

TL;DR

This paper systematically reviews the use of LSTM models for air quality time series prediction, analyzing existing methods, identifying research gaps, and suggesting future directions for improving prediction accuracy.

Contribution

It provides a comprehensive mapping of LSTM-based approaches in air quality prediction, highlighting gaps and potential research opportunities.

Findings

LSTM models are widely used for air quality prediction.

Research gaps exist in data diversity and model interpretability.

Future work should focus on hybrid models and real-time prediction.

Abstract

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.