9,657 research outputs found
LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data
In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requires considerable effort from domain experts.
An alternative approach is to use deep learning algorithms. Research has shown that modern deep neural networks are very effective in automated extraction of abstract features from raw data in classification tasks. Long short-term memory networks, or LSTMs in short, are a special kind of recurrent neural networks that are capable of learning long-term dependencies. These networks have proved to be especially effective in the classification of raw time-series data in various domains. This dissertation systematically investigates the effectiveness of the LSTM model for anomaly detection and classification in raw time-series sensor data.
As a proof of concept, this work used time-series data of sensors that measure blood glucose levels. A large number of time-series sequences was created based on a genuine medical diabetes dataset. Anomalous series were constructed by six methods that interspersed patterns of common anomaly types in the data. An LSTM network model was trained with k-fold cross-validation on both anomalous and valid series to classify raw time-series sequences into one of seven classes: non-anomalous, and classes corresponding to each of the six anomaly types.
As a control, the accuracy of detection and classification of the LSTM was compared to that of four traditional machine learning classifiers: support vector machines, Random Forests, naive Bayes, and shallow neural networks. The performance of all the classifiers was evaluated based on nine metrics: precision, recall, and the F1-score, each measured in micro, macro and weighted perspective.
While the traditional models were trained on vectors of features, derived from the raw data, that were based on knowledge of common sources of anomaly, the LSTM was trained on raw time-series data. Experimental results indicate that the performance of the LSTM was comparable to the best traditional classifiers by achieving 99% accuracy in all 9 metrics. The model requires no labor-intensive feature engineering, and the fine-tuning of its architecture and hyper-parameters can be made in a fully automated way. This study, therefore, finds LSTM networks an effective solution to anomaly detection and classification in sensor data
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction
Internet traffic volume estimation has a significant impact on the business
policies of the ISP (Internet Service Provider) industry and business
successions. Forecasting the internet traffic demand helps to shed light on the
future traffic trend, which is often helpful for ISPs decision-making in
network planning activities and investments. Besides, the capability to
understand future trend contributes to managing regular and long-term
operations. This study aims to predict the network traffic volume demand using
deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based
noise reduction, Empirical rule based outlier detection, and -Nearest
Neighbour (KNN) based outlier mitigation. In contrast to the former studies,
the proposed model does not rely on a particular EMD decomposed component
called Intrinsic Mode Function (IMF) for signal denoising. In our proposed
traffic prediction model, we used an average of all IMFs components for signal
denoising. Moreover, the abnormal data points are replaced by nearest data
points average, and the value for has been optimized based on the KNN
regressor prediction error measured in Root Mean Squared Error (RMSE). Finally,
we selected the best time-lagged feature subset for our prediction model based
on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information
Criterion (AIC) value. Our experiments are conducted on real-world internet
traffic datasets from industry, and the proposed method is compared with
various traditional deep sequence baseline models. Our results show that the
proposed EMD-KNN integrated prediction models outperform comparative models.Comment: 13 pages, 9 figure
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
Anomaly detection in time series is a complex task that has been widely
studied. In recent years, the ability of unsupervised anomaly detection
algorithms has received much attention. This trend has led researchers to
compare only learning-based methods in their articles, abandoning some more
conventional approaches. As a result, the community in this field has been
encouraged to propose increasingly complex learning-based models mainly based
on deep neural networks. To our knowledge, there are no comparative studies
between conventional, machine learning-based and, deep neural network methods
for the detection of anomalies in multivariate time series. In this work, we
study the anomaly detection performance of sixteen conventional, machine
learning-based and, deep neural network approaches on five real-world open
datasets. By analyzing and comparing the performance of each of the sixteen
methods, we show that no family of methods outperforms the others. Therefore,
we encourage the community to reincorporate the three categories of methods in
the anomaly detection in multivariate time series benchmarks
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