# Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air

arXiv: https://arxiv.org/abs/2104.03226 · HTML: https://mlsanity.com/paper/2104.03226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Improving performance of recurrent neural network with relu nonlinearity](https://mlsanity.com/paper/1511.03771/) (2015)
- [A 1d convolutional network for leaf and time series classification](https://mlsanity.com/paper/1907.00069/) (2019)
- [Detection and Statistical Modeling of Birth-Death Anomaly](https://mlsanity.com/paper/1906.11788/) (2019)

## Built on by (most cited first, 1 of 1)

- [Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly](https://mlsanity.com/paper/2405.13995/) (2024)

## Abstract (arXiv)

Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, its genuinely unpredictable to mimic subatomic communication in the air, which brings about off base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error.