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 | | Learning rate: | | 1: Load training set and testing set from | | 2: Randomly initialize weight | | 3: Begin Training | | 4: for [1, M] do | | 5: Forward passing as equations (12)–(17) | | 6: Calculate loss as equation (18) | | 7: Calculate gradients of weights | | 8: Backpropagation and update | | 9: End Training | | 10: Begin Testing: calculate predicted values with testing set | | 11: return predicted values , |
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