Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models
In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and for...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2023
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| author | Carbo‐Bustinza, Natalí Iftikhar, Hasnain Belmonte, Marisol Cabello-Torres, Rita Jaqueline Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk |
| author_browse | Belmonte, Marisol Cabello-Torres, Rita Jaqueline Carbo‐Bustinza, Natalí Huaman de la Cruz, Alex Ruben Iftikhar, Hasnain López Gonzales, Javier Linkolk |
| author_facet | Carbo‐Bustinza, Natalí Iftikhar, Hasnain Belmonte, Marisol Cabello-Torres, Rita Jaqueline Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk |
| author_sort | Carbo‐Bustinza, Natalí |
| collection | Producción científica |
| description | In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/425 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4252026-10-30T16:47:25ZShort-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series ModelsCarbo‐Bustinza, NatalíIftikhar, HasnainBelmonte, MarisolCabello-Torres, Rita JaquelineHuaman de la Cruz, Alex RubenLópez Gonzales, Javier Linkolkshort-term ozone concentration forecastingseasonal trend decomposition methodtime series modelshybrid modelsIn the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.Articlehttps://www.mdpi.com/2076-3417/13/18/10514/pdf?version=1695274721https://doi.org/10.3390/app13181051410.3390/app131810514https://hdl.handle.net/20.500.15037/425W4386929249enApplied Sciences2076-34172023-09-212026-06-13T08:13:18Z2026-06-13T08:13:18Z |
| spellingShingle | short-term ozone concentration forecasting seasonal trend decomposition method time series models hybrid models Carbo‐Bustinza, Natalí Iftikhar, Hasnain Belmonte, Marisol Cabello-Torres, Rita Jaqueline Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models |
| title | Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models |
| title_full | Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models |
| title_fullStr | Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models |
| title_full_unstemmed | Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models |
| title_short | Short-Term Forecasting of Ozone Concentration in Metropolitan Lima Using Hybrid Combinations of Time Series Models |
| title_sort | short term forecasting of ozone concentration in metropolitan lima using hybrid combinations of time series models |
| topic | short-term ozone concentration forecasting seasonal trend decomposition method time series models hybrid models |
| topic_facet | short-term ozone concentration forecasting seasonal trend decomposition method time series models hybrid models |
| url | https://www.mdpi.com/2076-3417/13/18/10514/pdf?version=1695274721 https://doi.org/10.3390/app131810514 https://hdl.handle.net/20.500.15037/425 |
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