title: Explanatory comparative analysis of time series forecasting algorithms for air quality prediction
metadata.name: dc.contributor.author
metadata.value: Qi, Dongfang
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-08-19T20:47:47Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-08-19T20:47:47Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Qi D., Bure V. M. Explanatory comparative analysis of time series forecasting algorithms for air quality prediction. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2024, vol. 20, iss. 2, pp. 206–219. https://doi.org/10.21638/spbu10.2024.206 (In Russian)
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu10.2024.206
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/47267
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This study explores the effectiveness of time series forecasting models for predicting air
  quality using datasets from a Purple Air Dual Laser Air Quality Sensor and the Kaggle
  Online platform. These datasets contain reliable and real sensor records, ensuring the richness
  of information required for environmental protection. The research focuses on identifying
  suitable forecast models for environmental analysis, including popular algorithm structures
  such as neural network models and ensemble models. Moreover, the study introduces the
  Explainable artificial intellect method to provide explanations for models with excellent
  performance indicators, thereby enhancing their trust and transparency. The performance of
  the models was evaluated using metrics such as mean absolute error, root mean square error,
  and coefficient of determination (R-squared). Results indicate that the neural network and
  ensemble models are effective in forecasting air quality time series. The study contributes
  to the body of knowledge on time series forecasting models and provides insights for future
  research in air quality prediction.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: air quality
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: time series forecasting
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: neural networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: ensemble models
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: explainable artificial intellect
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Explanatory comparative analysis of time series forecasting algorithms for air quality prediction
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: vestnik_spbu10_2024_206.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/47267/1/vestnik_spbu10_2024_206.pdf
resources.format: Adobe PDF
resources.size: 1,15 MB