# Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
## 2 Related Work  
Classical methods for handling time-series anomaly detection problems can be categorized into clustering [10], density-based [11], distance-based [12], and isolation-based [13] approaches. Despite making some progress in time-series anomaly detection, these methods have limitations such as suboptimal performance due to insufficient consideration of time dependencies and correlations between high-dimensional features. As deep neural networks continue to evolve and improve, researchers are increasingly exploring the use of deep neural networks to address time-series anomaly detection challenges. Unlike traditional mathematical modeling approaches, deep neural networks eliminate the need for explicit feature engineering, allowing them to better capture complex patterns in data. In current anomaly detection benchmarks, one common approach involves using recurrent neural networks (RNNs) [14] to identify pattern sequences and predict expected values. This is achieved by recognizing differences between predicted signals and actual signals to determine anomalies. Furthermore, researchers have proposed innovative approaches to address the challenges of unsupervised anomaly detection. For example, [PERSON] et al. (2015) introduced the AutoEncode ensemble method, an unsupervised anomaly detection approach. It determines the presence of anomalies by randomly adjusting the connection architecture of autoencoders and randomly dropping certain connections. This method achieves unsupervised training, overcoming the lack of labeled data. [PERSON] et al. (2016) integrated LSTM and AutoEncode, constructing an LSTM-AE performance evaluation model applied to the continuous monitoring of wind turbine states. They validated the effectiveness of this method on real wind turbine condition monitoring (CM) data. [PERSON] et al. (2017) employed a Long Short-Term Memory Variational Autoencoder (LSTM-VAE). They introduced a progress-based change prior, identifying anomalies by merging signals and reconstructing their expected distributions. If the reconstructed anomaly score exceeds a state-based threshold, it is considered an anomaly. Due to the capability of Convolutional Long Short-Term Memory (ConvLSTM) ([PERSON] et al., 2018) in modeling spatiotemporal correlations using convolutional layers instead of fully connected layers, [PERSON] et al. (2019) proposed a method called the C-LSTM neural network for effectively detecting anomalies in network traffic data. This method combines Convolutional Neural Networks (CNNs) ([PERSON] et al., 2019) for extracting spatial features and LSTM models for extracting temporal features. The DAGMM method proposed by [PERSON] et al. (2019) utilizes a deep autoencoder Gaussian Mixture Model for dimensionality reduction in feature space and uses a recursive network for time modeling. [PERSON] et al. 's MAD-GAN ([PERSON] et al., 2019) employs an LSTM-based GAN model to simulate the distribution of time series using a generator. This work not only utilizes prediction errors but also incorporates discriminator loss in anomaly scores. [PERSON] et al. proposed OmniAnomaly (2019), a random recursive neural network for multivariate time-series anomaly detection. The network learns robust representations of multivariate time series through random variable connections and plane normalization flow, determining anomalies using reconstruction probabilities. CAE-M ([PERSON] et al., 2019) uses a convolutional autoencoder memory network similar to MSCRED ([PERSON] et al., 2019). However, these recursive neural-network-based models often come with higher computational costs and face challenges in scalability. These innovative approaches bring new perspectives and solutions to the field of unsupervised anomaly detection. The recent USAD (2019) method takes an innovative approach, utilizing an autoencoder with two decoders and an adversarial training framework. By using a simple autoencoder, this method significantly reduces training time.