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

arXiv 2111.11848 ↗First posted 22 Nov 2021cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 22 Nov 2021.

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3 of 24 references.

References · 3

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2018EA-LSTM: Evolutionary Attention-based LSTM for Time Series Predictioncited by 26
  3. 2017A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Predictioncited by 8

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