ArticleScientific reports2023
Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Optimized hybrid neural hierarchical interpolation time series with STL for flow forecasting in hydroelectric power plants.Scientific reports · 2026Article
- Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data.Sensors (Basel, Switzerland) · 2025Article
- Machine learning enhanced grey box soft sensor for melt viscosity prediction in polymer extrusion processes.Scientific reports · 2025Article
- Prediction of mechanical characteristics of shearer intelligent cables under bending conditions.PloS one · 2025Article
- Decomposition-reconstruction-optimization framework for hog price forecasting: Integrating STL, PCA, and BWO-optimized BiLSTM.PloS one · 2025Article
- Research on information leakage in time series prediction based on empirical mode decomposition.Scientific reports · 2024Article
- Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss.Scientific reports · 2024Article
- Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model.Scientific reports · 2023Article
- Application of hybrid improved temporal convolution network model in time series prediction of river water quality.Scientific reports · 2023Article
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Authors and funding
2 authors.
Funding
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Abstract
A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal-trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM
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Registered trials
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