Forecasting of particulate matter with a hybrid ARIMA model based on wavelet transformation and seasonal adjustment

Aladağ, Erdinç

doi:10.1016/j.uclim.2021.100930

Cited by 54 publications

(17 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, correlation analysis and wavelet transform coherence analytical methods were used to investigate the relationships between the COVID-19 pandemic and air pollution. The t of data to normal distribution was determined with the Jarque-Bera test used for time series (Aladağ 2021). To investigate the correlation between variables without normal distribution, the non-parametric Kendall and Spearman rank correlation tests were used.…”

Section: Methodsmentioning

confidence: 99%

The nexus between air pollution and the COVID-19 pandemic in Turkey: Further evidence from wavelet coherence analysis

Aladağ

2023

Preprint

View full text Add to dashboard Cite

The effect of the ambient air quality on the COVID-19 pandemic is a topic that has attracted much attention and still remains of current interest. The study area of Turkey is one of the countries with high case numbers, but there is no detailed investigation dealing with it in the literature. For this reason, the correlation and nexus between COVID-19 cases and deaths in Turkey with the air pollutants of PM10, PM2.5, SO2, CO, NO2 and O3 were determined between 1 April and 31 July 2021 using the statistical methods of correlation and wavelet coherence analysis. According to the findings, for the COVID-19 pandemic parameters, there were positive significant correlations with PM2.5, SO2, CO, and NO2 and an inverse significant correlation with O3. Wavelet transform coherence revealed that air pollution, which has significant effects on human life, was one of the main determinants of the spread and mortality linked to the pandemic. Additionally, it is notable that the national air quality improved during full lockdown periods in the country. The findings obtained in this study are expected to attract the attention of legislating and enforcing authorities and support more decisive steps being taken to reduce environmental pollutants and to control air pollution.

show abstract

Section: Methodsmentioning

confidence: 99%

The nexus between air pollution and the COVID-19 pandemic in Turkey: Further evidence from wavelet coherence analysis

Aladağ

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The machine learning models such as Cascade-forward neural network requires 69 months of daily PM2.5, PM10, CO, SO2, and O3 pollutant concentration data for modeling (Zhao et al 2020). The wavelet-transform based hybrid ARIMA model requires monthly PM10 concentration from 2006 to 2016 for modeling (Aladag 2021).…”

Section: So2mentioning

confidence: 99%

An unequal new information priority grey forecasting air pollution urban model considering the regional socio-economic development

Dang

Wang

2023

Preprint

View full text Add to dashboard Cite

The prediction of air quality has been a hot research topic in environmental issues. Due to the obvious regional characteristics of air pollution in Handan, the prediction of the Air Quality Composite Index (AQCI) in Handan was carried out considering the socio-economic factors in Handan and the surrounding cities. Firstly, the main factors affecting the AQCI of Handan were screened from the added value of primary industry, added value of industry, added value of construction industry, added value of tertiary industry, number of resident population and vehicle ownership in Handan and its five surrounding cities using a grey correlation analysis model. Then, considering the variability of development trends and information priorities among different city factors, a grey unequal new information priority multivariate convolution prediction model (UNGMC(1,N)) is proposed, and the particle swarm algorithm is used to adaptively optimize the nonlinear model parameters. The model comparison results show that the stability and prediction accuracy of the proposed UNGMC(1,N) model are significantly higher than those of other grey models and some popular air quality prediction methods. According to the research results, the air quality of Handan city is mainly influenced by the number of permanent residents in Handan city, and the air quality of Handan city is negatively correlated with the number of permanent residents in Handan city. In terms of regional pollution, the air quality of Handan city shows a positive correlation with the added value of tertiary industry in the neighboring city of Anyang. In addition, Handan's air quality will continue to improve in the future with U-shaped improvement efficiency and 2024 will be a critical year for Handan's air quality improvement efficiency.

show abstract

“…Autoregressive Integrated Moving Averaged Model is a commonly used time series forecasting method. The core idea of the model is to find a suitable mathematical function to fit the linear relationship between the current time value, the past time value, and the random interference amount to infer the future value through the past value [7]. The essence of the ARIMA model is an improvement of the ARMA model, and its mathematical formula is:…”

Section: Arima Modelmentioning

confidence: 99%

Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model

Zhou¹,

Li²

2023

Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022

View full text Add to dashboard Cite

The net value of fund is affected in many ways, and researchers attempt to quantify these influences in order to predict future net value by developing various models. Current prediction models typically can only reflect the linear variation law, and their nonlinear characteristics are either poorly handled or selectively ignored, resulting in less accurate prediction results. Based on this, the ARIMA-LSTM hybrid model is used in this paper to predict funds. After preprocessing historical data, the ARIMA model is used to filter out the linear data characteristics, followed by the LSTM model to extract the nonlinear characteristics by residual, and finally superposition the respective prediction values of the two models was performed to obtain the hybrid model's prediction results. The paper's methodologies are empirically proven to be more accurate and applicable than typical fund forecast methods.

show abstract

Forecasting of particulate matter with a hybrid ARIMA model based on wavelet transformation and seasonal adjustment

Cited by 54 publications

References 42 publications

The nexus between air pollution and the COVID-19 pandemic in Turkey: Further evidence from wavelet coherence analysis

The nexus between air pollution and the COVID-19 pandemic in Turkey: Further evidence from wavelet coherence analysis

An unequal new information priority grey forecasting air pollution urban model considering the regional socio-economic development

Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model

Contact Info

Product

Resources

About