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Explainable Time-Series Prediction Using a Residual Network and Gradient-Based Methods
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DOI：10.1109/ACCESS.2022.3213926.png)
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摘要
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Researchers are employing deep learning (DL) in many fields, and the scope of its application is expanding. However, because understanding the rationale and validity of DL decisions is difficult, a DL model is occasionally called a black-box model. Here, we focus on a DL-based explainable time-series prediction model. We propose a model based on long short-term memory (LSTM) followed by a convolutional neural network (CNN) with a residual connection, referred to as the LSTM-resCNN. In comparison to one-dimensional CNN, bidirectional LSTM, CNN-LSTM, LSTM-CNN, and MTEX-CNN models, the proposed LSTM-resCNN performs best on the three datasets of fine dust (PM2.5), bike-sharing, and bitcoin. Additionally, we tested with Grad-CAM, Integrated Gradients, and Gradients, three gradient-based approaches for the model explainability. These gradient-based techniques combined very well with the LSTM-resCNN model. Variables and time lags that considerably influence the explainable time-series prediction can be identified and visualized using gradients and integrated gradients.
Keyword:
Convolutional neural networks
Predictive models
Feature extraction
Data models
Logic gates
Time series analysis
Data mining
Neural networks
Recurrent neural networks
Neural networks
recurrent neural networks
time series analysis
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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