Convolutional Neural Network

class dtaianomaly.anomaly_detection.ConvolutionalNeuralNetwork(window_size: str | int, error_metric: Literal['mean-absolute-error', 'mean-squared-error'] = 'mean-absolute-error', forecast_length: int = 1, kernel_size: int = 3, hidden_layers: list[int] = (64, 32), activation_function: Literal['linear', 'relu', 'sigmoid', 'tanh'] = 'relu', batch_normalization: bool = True, stride: int = 1, standard_scaling: bool = True, batch_size: int = 32, data_loader_kwargs: dict[str, any] = None, optimizer: Literal['adam', 'sgd'] | Callable[[any], Optimizer] = 'adam', learning_rate: float = 0.001, compile_model: bool = False, compile_mode: Literal['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs'] = 'default', n_epochs: int = 10, loss_function: Module = MSELoss(), device: str = 'cpu', seed: int = None)[source]

Use a convolutional neural network to detect anomalies.

The Convolutional Neural Network (CNN) is a neural network consisting of convolutional layers, each consisting of multiple kernels. Given some input, the convolutional layer computes the convolution of the input with each kernel to create an output. The input sequences are fed through multiple such convolutional layers, and the task is to forecast the time series. Hence, by computing the difference between the forecasted value and the actually observed values, the neural network can detect anomalies.

The architecture of the CNN consists of blocks, in which each block applies the following operations: convolutional layer \(\rightarrow\) batch normalization \(\rightarrow\) activation function \(\rightarrow\) average pooling. The first and final layers of the network has no batch normalization.

Parameters:

window_size (int or str) – The window size to use for extracting sliding windows from the time series. This value will be passed to compute_window_size().
error_metric ({"mean-absolute-error", "mean-squared-error"}, default="mean-absolute-error") – The error measure between the reconstructed window and the original window.
kernel_size (int, default=3) – The size of the kernels in the convolutional layers.
hidden_layers (list of ints, default=[64, 32]) – The number of kernels in each hidden layer. Must contain at least 1 value.
activation_function ({"linear", "relu", "sigmoid", "tanh"} default="relu") – The activation function to use at the end of each convolutional layer.
batch_normalization (bool = True,) – Whether to add batch normalization after each convolutional layer or not.
stride (int, default=1) – The stride, i.e., the step size for extracting sliding windows from the time series.
standard_scaling (bool, default=True) – Whether to standard scale each window independently, before feeding it to the network.
batch_size (int, default=32) – The size of the batches to feed to the network.
data_loader_kwargs (dictionary, default=None) – Additional kwargs to be passed to the data loader. For more information, see: https://docs.pytorch.org/docs/stable/data.html
optimizer ({"adam", "sgd"} or callable default="adam") – The optimizer to use for learning the weights. If “adam” is given, then the torch.optim.Adam optimizer will be used. If “sgd” is given, then the torch.optim.SGD optimizer will be used. Otherwise, a callable should be given, which takes as input the network parameters, and then creates an optimizer.
learning_rate (float, default=1e-3) – The learning rate to use for training the network. Has no effect if optimize is a callable.
compile_model (bool, default=False) – Whether the network architecture should be compiled or not before training the weights. For more information, see: https://docs.pytorch.org/docs/stable/generated/torch.compile.html
compile_mode ({"default", "reduce-overhead", "max-autotune", "max-autotune-no-cudagraphs"}, default="default") – Method to compile the architecture. For more information, see: https://docs.pytorch.org/docs/stable/generated/torch.compile.html
n_epochs (int, default=10) – The number of epochs for which the neural network should be trained.
loss_function (torch.nn.Module, default=torch.nn.MSELoss()) – The loss function to use for updating the weights.
device (str, default="cpu") – The device on which te neural network should be trained. For more information, see: https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch-device
seed (int, default=None) – The seed used for training the model. This seed will update the torch and numpy seed at the beginning of the fit method.

window_size_

The effectively used window size for this anomaly detector.

Type:: int

optimizer_

The optimizer used for learning the weights of the network.

Type:: torch.optim.Optimizer

neural_network_

The PyTorch network architecture.

Type:: torch.nn.Module

Examples

>>> from dtaianomaly.anomaly_detection import ConvolutionalNeuralNetwork
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> cnn = ConvolutionalNeuralNetwork(10, seed=0).fit(x)
>>> cnn.decision_function(x)
array([0.07708263, 0.07708263, 0.06242053, ..., 0.1827196 , 0.2396274 ,
       0.06390759]...)