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Timeseries classification from scratch

Based on the Timeseries classification from scratch example on keras.io created by hfawaz.

Model description

The model is a Fully Convolutional Neural Network originally proposed in this paper. The implementation is based on the TF 2 version provided here. The hyperparameters (kernel_size, filters, the usage of BatchNorm) were found via random search using KerasTuner.

Intended uses & limitations

Given a time series of 500 samples, the goal is to automatically detect the presence of a specific issue with the engine.

The data used to train the model was already z-normalized: each timeseries sample has a mean equal to zero and a standard deviation equal to one.

Training and evaluation data

The dataset used here is called FordA. The data comes from the UCR archive. The dataset contains:

  • 3601 training instances
  • 1320 testing instances

Each timeseries corresponds to a measurement of engine noise captured by a motor sensor.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

name learning_rate decay beta_1 beta_2 epsilon amsgrad training_precision
Adam 9.999999747378752e-05 0.0 0.8999999761581421 0.9990000128746033 1e-07 False float32

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Model reproduced by Edoardo Abati
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Space using keras-io/timeseries-classification-from-scratch