Abstract
Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.
Highlights
The data we collect in the existing system is complex time-series data, such as air pollution data [1], i.e., PM2.5, PM10, and O3. e forecasting of these pollution content is essential for air quality control
We use 5 indicators to assess the performance of our models, including root means square error (RMSE), normalized mean square error (NRMSE), mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), and Pearson correlation coefficient (R). e smaller the first four indicators are, the more accurate the forecasting is
We evaluate the hyperparameters of the WD-Gated Recurrent Unit (GRU) forecasting model optimized by the Bayesian sequence model-based optimization (SMBO) algorithm
Summary
The data we collect in the existing system is complex time-series data, such as air pollution data [1], i.e., PM2.5, PM10, and O3. e forecasting of these pollution content is essential for air quality control. As to the PM2.5 forecasting problem, accurate multisteps forecasting is more meaningful because it can provide faster response time to control and manage air quality. The data-driven learning method [3] is more adaptable for modeling based on the historical data without requiring prior knowledge. Erefore, data-driven learning methods, such as the deep learning method, perform better in nonlinear complex dynamic forecasting tasks. A data-driven model is proposed to the multisteps ahead forecast.
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