Research Article
Seismic Events Prediction Using Deep Temporal Convolution Networks
| | Input: Multivariable time series (training set and testing set) | | | Output: Predicted values , trained network weights: | | | Parameters: | | | Kernel size: k | | | Training iterations: M | | | Training batch size: B | | | Dilated coefficient: d | | | Learning rate: | | | Number of stacks: s | | | 1: Load training set and testing set from | | | 2: Randomly initialize weight w | | | 3: Begin Training | | | 4: for [1, M] do | | | 5: Forward passing as equations (2)–(4) | | | 6: Calculate loss as equation (6) | | | 7: Calculate gradients of weights as equation (9) | | | 8: Backpropagation and update w | | | 9: End Training | | | 10: Begin Testing: calculate predicted values with testing set | | | 11: return predicted values , |
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