Today, I would like to introduce a powerful deep learning model: CNN-LSTM-Attention!
This model combines three different types of neural network architectures, fully exploiting the spatial and temporal information in the data. It not only captures the local features and long-term dependencies of the data but also automatically focuses on the most important parts of the input data, playing a crucial role in improving prediction accuracy and robustness.
Therefore, it is also the preferred choice for solving time series prediction and other sequence data processing tasks, with research on it gaining popularity in major conferences, such as the CBLA model with nearly 100% classification accuracy.
If any researchers are interested and need references in this area for ideas, I also provide 9 latest papers on CNN-LSTM-Attention, along with open-source code, hoping to contribute to your papers.
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A Deep LSTM-CNN Based on Self-Attention Mechanism with Input Data Reduction for Short-Term Load Forecasting
Method: The paper introduces a deep learning model based on Long Short-Term Memory networks, Convolutional Neural Networks, and Self-Attention Mechanism (SAM) for short-term load forecasting (STLF). Experiments show that this model improves prediction accuracy while reducing input data, outperforming traditional benchmark models by over 10%.
Innovations:
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First time using LSTM-CNN combined SAM model for short-term load forecasting (STLF). -
Achieving a hybrid prediction framework based solely on load data using output dimensions. -
Innovatively using convolutional kernels to extract user randomness, addressing non-stationary characteristics.
Prediction of Remaining Useful Life of Aero-Engines Based on CNN-LSTM-Attention
Method: The paper presents a prediction method that combines Convolutional Neural Networks, Long Short-Term Memory networks, and Attention Mechanism to predict the Remaining Useful Life (RUL) of aero-engines. The model first uses CNN to extract features from the input data, then inputs the extracted data into the LSTM network model, and finally predicts the RUL of the aero-engine by incorporating the attention mechanism.
Innovations:
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An innovative combined prediction method integrating Convolutional Neural Networks, Long Short-Term Memory networks, and Self-Attention Mechanism for more accurate prediction of aero-engine RUL. -
Introduced Self-Attention Mechanism into the CNN-LSTM model, allowing the LSTM component to focus more on the important parts of the features reconstructed by CNN in the final prediction.
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Attention-Based CNN-LSTM and XGBoost Hybrid Model for Stock Prediction
Method: This paper proposes a hybrid model based on Attention Mechanism, CNN-LSTM, and XGBoost for predicting stock prices in the Chinese stock market. By integrating ARIMA models and the nonlinear relationships of neural networks, it addresses the difficulties traditional time series models face in capturing nonlinearity, improving prediction accuracy and helping investors achieve profit growth and risk avoidance.
Innovations:
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Proposed a hybrid model combining Attention Mechanism, CNN-LSTM, and XGBoost, significantly improving the accuracy of stock price predictions. -
Employing a pre-training and fine-tuning framework, first extracting deep features from raw stock data using Attention-based CNN-LSTM model, then fine-tuning using XGBoost model. -
Using ARIMA model for preprocessing stock data, then inputting the preprocessed data into neural networks or XGBoost for analysis.
Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model
Method: The paper proposes a hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) with Attention Mechanism to detect motor faults. This hybrid model predicts potential anomalies in motors through time series analysis, enabling predictive maintenance of the motors.
Innovations:
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Proposed a hybrid architecture combining LSTM and CNN, introducing Attention Mechanism and Gated Residual Network (GRN), significantly improving the accuracy of time series predictions, especially excelling in predicting extreme events. -
Applied quantile regression at the network output end to handle uncertainties present in the data. -
Employed Empirical Wavelet Transform (EWT) and Savitzky-Golay filter to reduce noise in the signal and extract relevant features.
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