Predicting hourly ozone concentrations using wavelets and ARIMA models

Salazar, Ledys; Nicolis, Orietta; Ruggeri, Fabrizio; Kiseľák, Jozef; Stehlík, Milan

doi:10.1007/s00521-018-3345-0

Cited by 21 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both the ARIMA model and the Wavelet decomposition methods have different tendencies to deal with linear and non-linear features of data, so the coupled models proposed in this study consists of forecasting by ARIMA models on the time series data refined by wavelet decomposition methods. Thus, the coupled models can improve forecasting performance by modeling linear and non-linear components of data [48] .…”

Section: Hybrid Prediction Modelmentioning

confidence: 99%

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Singh

Parmar

Kumar

et al. 2020

Chaos, Solitons & Fractals

117

101

View full text Add to dashboard Cite

Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

show abstract

Section: Hybrid Prediction Modelmentioning

confidence: 99%

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Singh

Parmar

Kumar

et al. 2020

Chaos, Solitons & Fractals

117

101

View full text Add to dashboard Cite

show abstract

“…Many researchers have investigated the performance of classical time series models for forecasting O 3 concentrations (Salazar et al 2019). For example, Kumar et al (2004) studied the autoregressive integrated moving average (ARIMA) model to forecast the daily surface O 3 concentration.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Hourly Ozone Concentration Forecasting Using Functional Data Approach

Shah,

Gul,

Ali

et al. 2024

Econometrics

View full text Add to dashboard Cite

Air pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims to use the functional time series approach to model ozone concentrations, a method less explored in the literature, and compare it with traditional time series and machine learning models. To this end, the ozone concentration hourly time series is first filtered for yearly seasonality using smoothing splines that lead us to the stochastic (residual) component. The stochastic component is modeled and forecast using a functional autoregressive model (FAR), where each daily ozone concentration profile is considered a single functional datum. For comparison purposes, different traditional and machine learning techniques, such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR), neural network autoregressive (NNAR), random forest (RF), and support vector machine (SVM), are also used to model and forecast the stochastic component. Once the forecast from the yearly seasonality component and stochastic component are obtained, both are added to obtain the final forecast. For empirical investigation, data consisting of hourly ozone measurements from Los Angeles from 2013 to 2017 are used, and one-day-ahead out-of-sample forecasts are obtained for a complete year. Based on the evaluation metrics, such as R2, root mean squared error (RMSE), and mean absolute error (MAE), the forecasting results indicate that the FAR outperforms the competitors in most scenarios, with the SVM model performing the least favorably across all cases.

show abstract

“…In order to compensate for the large error of a single-model, hybrid prediction models were created. Hybrid models [12] refer to models generated by combining signal decomposition techniques with other prediction models, which are characterized by further decomposing the nonlinear original time series into more stable and regular subseries, and obtaining the final prediction results by aggregating the predicted values of all subseries.…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet decomposition technique is a widely used signal processing method, commonly used in prediction models of time series [13], and its basic principle is to decompose a non-smooth discrete time series into a combination of sequences with different high-frequency detail components and a low-frequency approximate component, and the number of high-frequency detail components depends on the number of layers of wavelet decomposition. Ledys Salazar et al, [12] fused wavelet decomposition and auto regressive integrated moving average model (ARIMA), for predicting hourly O 3 concentrations. To address the problem of low horizontal and directional prediction accuracy of nonlinear AQI sequences, Jiang et al, [14] proposed a hybrid model based on WD, multidimensional scaling and K-means (MSK) clustering methods and an improved extreme learning machine (ELM) method.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Air Quality Prediction Based on Data Decomposition

et al. 2021

View full text Add to dashboard Cite

Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R2) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R2 by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method.

show abstract

Predicting hourly ozone concentrations using wavelets and ARIMA models

Cited by 21 publications

References 14 publications

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Short-Term Hourly Ozone Concentration Forecasting Using Functional Data Approach

A Hybrid Model for Air Quality Prediction Based on Data Decomposition

Contact Info

Product

Resources

About