Anomaly detection in time series data using a combination of wavelets, neural networks and Hilbert transform

Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.

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Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

Expert Systems with Applications ◽

10.1016/j.eswa.2017.04.028 ◽

2017 ◽

Vol 85 ◽

pp. 292-304 ◽

Cited By ~ 40

Author(s):

Stratis Kanarachos ◽

Stavros-Richard G. Christopoulos ◽

Alexander Chroneos ◽

Michael E. Fitzpatrick

Keyword(s):

Neural Networks ◽

Time Series ◽

Deep Learning ◽

Hilbert Transform ◽

Time Series Data ◽

Learning Algorithm ◽

Series Data ◽

Deep Learning Algorithm

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An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.363 ◽

2016 ◽

Vol 136 (3) ◽

pp. 363-372

Author(s):

Takaaki Nakamura ◽

Makoto Imamura ◽

Masashi Tatedoko ◽

Norio Hirai

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Detection Method ◽

Series Data

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Neural Network Techniques for Time Series Prediction: A Review

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.3.281 ◽

2019 ◽

Vol 3 (3) ◽

Author(s):

Muhammad Faheem Mushtaq ◽

Urooj Akram ◽

Muhammad Aamir ◽

Haseeb Ali ◽

Muhammad Zulqarnain

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Data ◽

Weather Prediction ◽

Time Series Prediction ◽

Series Data ◽

Prediction Problem ◽

Neural Network Models ◽

Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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A review of irregular time series data handling with gated recurrent neural networks

Neurocomputing ◽

10.1016/j.neucom.2021.02.046 ◽

2021 ◽

Vol 441 ◽

pp. 161-178

Author(s):

Philip B. Weerakody ◽

Kok Wai Wong ◽

Guanjin Wang ◽

Wendell Ela

Keyword(s):

Neural Networks ◽

Time Series ◽

Recurrent Neural Networks ◽

Time Series Data ◽

Series Data ◽

Data Handling

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LRZ Convolution: An Algorithm for Automatic Anomaly Detection in Time-series Data

32nd International Conference on Scientific and Statistical Database Management ◽

10.1145/3400903.3400904 ◽

2020 ◽

Author(s):

Arunprasad P. Marathe

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Series Data

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Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽

10.3390/w13121633 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1633

Author(s):

Elena-Simona Apostol ◽

Ciprian-Octavian Truică ◽

Florin Pop ◽

Christian Esposito

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Change Point ◽

Time Series Data ◽

Multivariate Time Series ◽

Change Point Detection ◽

Change Points ◽

Series Data ◽

Prediction And Forecasting ◽

Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

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