The impact of data imputation on air quality prediction problem
Van Hua1,2,3, Thu Nguyen4, Minh-Son Dao5
1Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam.
Plos One
|September 12, 2024
Summary
Accurate air quality prediction is vital for environmental health. This study reviews missing data imputation techniques for time series air quality data, evaluating eight methods to guide practitioners.
Area of Science:
- Environmental Science
- Data Science
- Time Series Analysis
Background:
- Accurate air quality prediction is crucial for public health and environmental policy.
- Air quality data is often time-series based, frequently containing missing values.
- Handling missing data is essential for reliable air quality analysis and prediction.
Purpose of the Study:
- To provide a comprehensive review of air quality prediction and missing data imputation techniques for time series data.
- To empirically evaluate the performance of eight different imputation methods on diverse air quality datasets.
- To offer practical recommendations for handling missing time series data in environmental contexts.
Main Methods:
- Literature review of air quality prediction and time series imputation methods.
- Empirical assessment of eight imputation techniques: mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer.
- Evaluation using various global air quality datasets.
Main Results:
- The study systematically compares the effectiveness of different imputation methods on air quality time series data.
- Performance variations among imputation techniques are analyzed across diverse datasets.
- Identification of methods suitable for addressing missing values in environmental data.
Conclusions:
- The choice of imputation method significantly impacts air quality data analysis and prediction accuracy.
- Empirical evaluation provides insights into the strengths and weaknesses of various techniques.
- Recommendations are provided to aid practitioners in selecting appropriate imputation strategies for environmental time series data.
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