时间序列的通用深度自动编码器matlab代码.zip

# Matlab-AE_MVTS Generic Deep Autoencoder for Time-Series This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural networks - the encoder and the decoder. The following layers can be combined and stacked to form the neural networks which form the encoder and decoder: - LSTM (Long-short term memory layers), - Bi-LSTM (Bi-directional long-short term memory layers), - FC with ReLU (Fully connected layers followed by a rectified linear unit). There are two types of autoencoders available: - AE (autoencoder) - VAE (variational autoencoder) The autoencoders can be easily parametrized using hyperparameters. This toolbox is also mirrored to [MatlabFileExchange](https://de.mathworks.com/matlabcentral/fileexchange/111110-generic-deep-autoencoder-for-time-series) ------------------------------------------------------------------------------------------- # Publishing information This code can also be considered as supplemental Material to the Paper: "Hybrid Machine Learning for Anomaly Detection in Industrial Time-Series Measurement Data" by: Anika Terbuch, Paul O'Leary and Peter Auer Mai 2022 Link to paper: https://ieeexplore.ieee.org/document/9806663 # Example Plot This plot shows the input and output of a multivariate time-series to a variational autoencoder built with this framework  ------------------------------------------------------------------------------------------- # Class descriptions The toolbox includes the following two classes: 1) AutoencoderDeep: generic framework for creating autoencoders suitable for multivariate time-series data 2) HyperparametersAED: the class used to parametrize objects of the class AutoencoderDeep. ------------------------------------------------------------------------------------------- For easier and more convinient use the classes are wrapped into functions: The following functions are provided: -trainAutoencoderDeep: trains an autoencoder using the data provided. -predictAutoencoderDeep: returns an prediction of the autoencoder passed to the function on the data provided to the function. ## AutoencoderDeep ad 1) The class AutoencoderDeep contains the following functions: - decodingAED: decodes the latent representation back into the original domain. - ELBOloss: calculates the Evidence Lower Bound (ELBO) of given data, given latent encoding and the weighting factor of the two terms of the loss function. - encodingAED: performs an encoding into the latent space of a trained AutoencoderDeep on the samples provided to the function. - gradientsRecErr: loss function of the AE - minimize the reconstruction loss evaluates the encoder and decoder on the passed data and calculates the gradients of the learnable parameters of the network. - gradientsRecErrAndKL: loss function of the VAE - maximize the evidence lower bound evaluates the encoder and decoder on the passed data and calculates the gradients of the learnables of the network. - layerArray2dlnetwork: converts a layer array to a dl-network. - reconstructionAED: performs the reoncustriction (encoding followed by decoding) on the given data with a trained AutoencodeDeep. - reconstructionErrorPerSampleAEDvariableLength: given the reconstructed signal and the real signal the reconstruction error (1- norm) normalized by the varying length of the time series is calculated. - samplingVAE: performs the encoding into the latent space followed by the reparametrization trick used in the calculation of latent representations when using VAEs. -setUpAndTrainAED: creates the encoder and decoder networks and trains them accoding to the properties specified in the hyperparameter struct. -setUpDecoderAED: sets up a neural network that forms the decoder of the network with the layer-types and number of neurons specified in the hyperparameter struct. -setUpEncoderAED: sets up a neural network that forms the encoder of the network with the layer-types and number of neurons specified in the hyperparameter struct. -setUpEncoderDecoderAED: function calls to functions used for setting up the encoder and decoder networks. -squaredReconstructionErrorPerSampleAEDvariableLength: given the reconstructed signal and the real signal the reconstruction error (2-norm) normalized by the varying length of the time series is calculated. -trainingLoopAED: training loop for training the encoder and decoder simultaneously based on ADAM, training can be performed using CPU-units or GPU- units. -varSeqLen2dlarray: creates mini-batches of dl-arrays with minimum resampling. ## HyperparametersAED ad 2) The class HyperparametersAED contains the following functions: -setDefaultHyperparametersAED: initializes the parameters to default values at instantiation of an object -setHyperparametersAED: can be used to change the hyperparameters of the AutoencoderDeep by passing the hyperaramter names to change and their values ('name value pairs') to this function. ------------------------------------------------------------------------------------------- This derived class is tailored to handle data shown in https://de.mathworks.com/help/deeplearning/ug/time-series-anomaly-detection-using-deep-learning.html https://de.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html and https://de.mathworks.com/help/deeplearning/ug/sequence-to-one-regression-using-deep-learning.html?searchHighlight=WaveformData&s_tid=srchtitle_WaveformData_3 The following impelementation was created using Matlab 2022a and 2022b.