Classifier

ConvTimeNetClassifier

ConvTimeNet for time series classification.

Quickstart

python

from sktime.classification.deep_learning.convtimenet import ConvTimeNetClassifier

estimator = ConvTimeNetClassifier(d_model, patch_size, patch_stride, dropout=0, d_ff=128, dw_ks=3, device='cpu', num_epochs=16, batch_size=8, criterion=None, criterion_kwargs=None, optimizer=None, optimizer_kwargs=None, lr=0.001, verbose=False, random_state=None)

Parameters(16)

d_modelint: Hidden dimension size for model processing.
patch_sizeint: Size of patches for sequence splitting.
patch_strideint: Stride length for patch creation.
dropoutfloat, optional (default=0): Dropout rate to apply to layers.
d_ffint, optional (default=128): Dimension of feedforward network.
dw_ksint or list, optional (default=3): Depthwise convolution kernel size(s). Can be a single int or list of ints.
devicestr, optional (default=”cpu”): Device to use for computation (“cpu” or “cuda”).
num_epochsint, optional (default=16): The number of epochs to train the model.
batch_sizeint, optional (default=8): The size of each mini-batch during training.
criterioncallable, optional (default=None): The loss function to use. If None, CrossEntropyLoss will be used.
criterion_kwargsdict, optional (default=None): Additional keyword arguments to pass to the loss function.
optimizerstr, optional (default=None): The optimizer to use. If None, Adam will be used.
optimizer_kwargsdict, optional (default=None): Additional keyword arguments to pass to the optimizer.
lrfloat, optional (default=0.001): The learning rate to use for the optimizer.
verbosebool, optional (default=False): Whether to print progress information during training.
random_stateint, optional (default=None): Seed to ensure reproducibility.

Examples

>>> from sktime.classification.deep_learning import ConvTimeNetClassifier
>>> import numpy as np
>>> # Create a sample multivariate time series dataset
>>> # 48 samples, 3 variables, length 128
>>> X = np. random. randn (16 * 3, 3, 128). astype ("float32")
>>> y = np. array ([0, 1, 2 ] * 16) # 3 classes
>>> # Create and fit the classifier
>>> clf = ConvTimeNetClassifier (
... patch_size = 4,
... patch_stride = 2,
... d_model = 64,
... d_ff = 128,
... dw_ks = [5, 7, 9 ],
... batch_size = 8,
... device = "cpu",
... random_state = 10
... )
>>> clf. fit (X, y) ConvTimeNetClassifier(
... )
>>> # Make predictions
>>> y_pred = clf. predict (X)
>>> y_proba = clf. predict_proba (X)

References

[1 ] Cheng, M., Yang, J., Pan, T., Liu, Q., & Li, Z. (2024). ConvTimeNet: A deep hierarchical fully convolutional model for multivariate time series analysis. arXiv preprint arXiv:2403.01493. https://arxiv.org/abs/2403.01493 [2 ] https://github.com/Mingyue-Cheng/ConvTimeNet