Time series prediction of the chemical components of PM2.5 based on a deep learning model

Liu, Kai; Zhang, Yuanhang; He, Huan; Xiao, Hui; Wang, Siyuan; Zhang, Yuteng; Li, Huiming; Qian, Xin

doi:10.1016/j.chemosphere.2023.140153

Cited by 3 publications

(1 citation statement)

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“…Meanwhile, PM2.5 is our feature due to the crucial environmental parameter directly impacting air quality and public health. In addition, PM2.5 has been used by many researchers as an essential pollution level to forecast air quality [39], [40], [45], [46]. The result showed that the model achieved a relatively low training loss with the 0.0014 value.…”

Section: ) Single-outputmentioning

confidence: 99%

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

Hartomo,

Lopo,

Purnomo

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Multi-output Time series forecasting is a complex problem that requires handling interdependencies and interactions between variables. Traditional statistical approaches and machine learning techniques often struggle to predict such scenarios accurately. Advanced techniques and model reconstruction are necessary to improve forecasting accuracy in complex scenarios. Objective: This study proposed an Encoder-Decoder network to address multi-output time series forecasting challenges by simultaneously predicting each output. This objective is to investigate the capabilities of the Encoder-Decoder architecture in handling multi-output time series forecasting tasks. Methods: This proposed model utilizes a 1-Dimensional Convolution Neural Network with Bidirectional Long Short-Term Memory, specifically in the encoder part. The encoder extracts time series features, incorporating a residual connection to produce a context representation used by the decoder. The decoder employs multiple unidirectional LSTM modules and Linear transformation layers to generate the outputs each time step. Each module is responsible for specific output and shares information and context along the outputs and steps. Results: The result demonstrates that the proposed model achieves lower error rates, as measured by MSE, RMSE, and MAE loss metrics, for all outputs and forecasting horizons. Notably, the 6-hour horizon achieves the highest accuracy across all outputs. Furthermore, the proposed model exhibits robustness in single-output forecast and transfer learning, showing adaptability to different tasks and datasets. Conclusion: The experiment findings highlight the successful multi-output forecasting capabilities of the proposed model in time series data, with consistently low error rates (MSE, RMSE, MAE). Surprisingly, the model also performs well in single-output forecasts, demonstrating its versatility. Therefore, the proposed model effectively various time series forecasting tasks, showing promise for practical applications. Keywords: Bidirectional Long Short-Term Memory, Convolutional Neural Network, Encoder-Decoder Networks, Multi-output forecasting, Multi-step forecasting, Time-series forecasting

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