# Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM

arXiv: https://arxiv.org/abs/2211.09814 · HTML: https://mlsanity.com/paper/2211.09814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Forecasting Economics and Financial Time Series: ARIMA vs. LSTM](https://mlsanity.com/paper/1803.06386/) (2018)
- [Accelerating DNA Sequence Analysis using Intel Xeon Phi](https://mlsanity.com/paper/1506.08612/) (2015)
- [CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi](https://mlsanity.com/paper/1702.07908/) (2017)
- [Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach](https://mlsanity.com/paper/2106.01441/) (2021)
- [Real-time Air Pollution prediction model based on Spatiotemporal Big data](https://mlsanity.com/paper/1805.00432/) (2018)
- [A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities](https://mlsanity.com/paper/1809.00238/) (2018)

## Abstract (arXiv)

Air pollution is a worldwide issue that affects the lives of many people in urban areas. It is considered that the air pollution may lead to heart and lung diseases. A careful and timely forecast of the air quality could help to reduce the exposure risk for affected people. In this paper, we use a data-driven approach to predict air quality based on historical data. We compare three popular methods for time series prediction: Exponential Smoothing (ES), Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM). Considering prediction accuracy and time complexity, our experiments reveal that for short-term air pollution prediction ES performs better than ARIMA and LSTM.