Air quality prediction using CT-LSTM

Wang, Jingyang; Li, Jiazheng; Wang, Xiaoxiao; Wang, Jue; Huang, Min

doi:10.1007/s00521-020-05535-w

Cited by 73 publications

(24 citation statements)

References 38 publications

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“…LSTM is better than the traditional recurrent neural network [24,25]. It overcomes the problem of gradient disappearance or gradient explosion [26]. Many financial time series studies use LSTM modelling [27].…”

Section: Related Workmentioning

confidence: 99%

A Stock Closing Price Prediction Model Based on CNN‐BiSLSTM

Wang

Cao

et al. 2021

Complexity

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As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error (MAE), root-mean-squared error (RMSE), and R-square (R2) evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.

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Section: Related Workmentioning

confidence: 99%

A Stock Closing Price Prediction Model Based on CNN‐BiSLSTM

Wang

Cao

et al. 2021

Complexity

Self Cite

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“…The model stores the information contained in the preorder data by using LSTM, while adjusts the basic time data sequence by Kalman filtering. Jingyang Wang [10] established CT-LSTM by combining CT (Chi-square Test) and LSTM. CT is used to determine the influencing factors of air quality which can help improving the accuracy and performance of prediction.…”

Section: Prediction Based On Lstm Modelmentioning

confidence: 99%

“…Where, is the attention weight of the sequence at time . According to these attention weights, a new sequence with attention weights can be calculated, as shown in Equation (10).…”

Section: Multi-factor Prediction Model Based On Lstm With Attention Mechanismmentioning

confidence: 99%

“…However, the existing air quality prediction methods cannot effectively capture the complex nonlinearity of air quality, like PM2.5 concentrations. The prediction models based on deep learning [9][10][11][12][13][14] can extract the features existing in the air quality data and can achieve higher prediction accuracy. Some methods [15][16][17][18][19][20][21][22][23][24][25] simulate the temporal and spatial dependence of air quality data at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Air Quality Prediction Based on Integrated Dual LSTM Model

Chen

Guan

2021

IEEE Access

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Air quality prediction is an important reference for meteorological forecast and air controlling, but over fitting often occurs in prediction algorithms based on a single model. Aiming at the complexity of air quality prediction, a prediction method based on integrated dual LSTM (Long Short-Term Memory) model was proposed in this paper. Firstly, the Seq2Seq (Sequence to Sequence) technology is used to establish a single-factor prediction model which can obtain the predicted value of each component in air quality data, independently. Each component of air quality is regarded as time series data in the forecasting process. Then, the LSTM model with attention mechanism is used as the multi-factor prediction model. The influencing factors of air quality, like the data of neighboring stations and weather data, are considered in the model. Finally, XGBoosting (eXtreme Gradient Boosting) tree is used to integrate two models. The final prediction results can be obtained by accumulating the predicted values of the optimal subtree nodes. Through evaluation and analysis using five evaluation methods, the proposed method has better performance in terms of error and model expression power. Compared with other various models, the precision of prediction data has been greatly improved in our model.

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“…W f , W i , W C , and W o are the weights applied to the concatenation of the new input The purpose of LSTM neural network design is to overcome the issues of gradient vanishing and gradient explosion, which are encountered when simple RNNs handle long-term dependent time series. The LSTM neural network model adds an input gate, an output gate and a forget gate [3,43]. The settings of relevant LSTM parameters are shown in Table 1.…”

Section: Lstm Modelmentioning

confidence: 99%

Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

Zhao

Yang

et al. 2021

Remote Sensing

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The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

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Air quality prediction using CT-LSTM

Cited by 73 publications

References 38 publications

A Stock Closing Price Prediction Model Based on CNN‐BiSLSTM

A Stock Closing Price Prediction Model Based on CNN‐BiSLSTM

Air Quality Prediction Based on Integrated Dual LSTM Model

Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

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