A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data

Gopali, Saroj; Abri, Faranak; Siami‐Namini, Sima; Namin, Akbar Siami

doi:10.1109/bigdata52589.2021.9671488

Cited by 34 publications

(11 citation statements)

References 14 publications

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“…Therefore, the difference in performance between TCN and criterion models is not significant. This finding is similar to (Gopali et al, 2022) and (Sadique and Sengupta, 2021) in a different case. Previous studies already confirm that convolutional network architecture is better than generic recurrent network architecture for sequence modeling across different tasks (Bai et al, 2018).…”

Section: Discussionsupporting

confidence: 88%

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

RYAN¹,

SAPUTRO²,

SOPAHELUWAKAN³

2022

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Wind shear is one of the dangerous meteorological phenomena for aviation. This phenomenon is significant, especially at the lower level. The duration of wind shear events varies greatly, ranging from short to long. The best way to avoid accidents caused by wind shear is by predicting the event and the duration. Recent studies use Machine Learning (ML) as a nonlinear geostatistical method to predict wind shear utilizing wind observing instruments data. The data is conditioned into temporal data which is fed to the ML model. However, the ML model used is not a temporal ML model for time-series data but a generic model for a common type of data. Many studies claimed temporal models are better than generic ones to tackle temporal data. In this study, we propose Temporal Convolutional Network (TCN) to predict incoming wind shear duration and occurrence using an anemometer sensor network i.e., Low-level Wind Shear Alert System (LLWAS). The wind shear occurrence is derived from wind shear duration prediction. The proposed model is compared with other temporal models, i.e., Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Different schemes of total predictor were tested to find the best predictor scheme for wind shear prediction. To measure the performance of all models in all schemes, accuracy, False Alarm Ratio (FAR), Probability of Detection (POD), and Root Mean Squared Error (RMSE) metrics are used. The result is TCN dominating almost in all metrics used i.e., Accuracy, FAR, and RMSE for all schemes against LSTM and GRU. Scheme with 4 predictors proved to bring the best performance of all models for wind shear duration prediction. The result proves TCN is the best temporal model for wind shear forecasting using LLWAS. For better wind shear duration prediction, the best scheme choice is the 4-predictor scheme.

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Section: Discussionsupporting

confidence: 88%

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

RYAN¹,

SAPUTRO²,

SOPAHELUWAKAN³

2022

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“…For this reason, the recurrent neural network (RNN) and its variants, such as the gated recurrent unit (GRU) [59] , and LSTM [112] , [113] incorporate hidden states which are generated by sequential information, thus capturing the underlying distribution of such datasets [91] , [114] , [115] . However, these methods are often computationally expensive, and training is slow [93] , [116] , [117] . This led to the development of the temporal convolutional network (TCN) for sequence modelling based on dilated causal 1-D convolutional layers [93] .…”

Section: Protein Representationmentioning

confidence: 99%

ProtInteract: A deep learning framework for predicting protein–protein interactions

Soleymani

Paquet

Viktor

et al. 2023

Computational and Structural Biotechnology Journal

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“…Gopali et al [7] compared the performance on RNN-based LSTM and CNN-based TCN models in the context of anomaly detection in a multivariate time series. The authors showed that the TCN models outperform other models with an F1 score of 0.92.…”

Section: Related Workmentioning

confidence: 99%

“…This work is an extension to our previous work [7], where we compared CNN and RNN-based models through classifications. Here, we extend our approach by considering different types of RNN models and also treating the problem as an estimation problem.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem, researchers have designed and tested a number of cutting-edge models. Examples include deep learningbased models such as convolutional neural networks (CNN) [4], graph-based modeling [5], the Graph Attention Network [6], and the Temporal Convolutional Network (TCN) [7]. In these approaches, the primary approach is the modeling of the problem through time-series analysis and then detecting what is called anomaly in the time-series streaming data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Time-Series Analysis for Detecting Anomalies in Internet of Things

Gopali

Namin

2022

Electronics

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Anomaly detection in time-series data is an integral part in the context of the Internet of Things (IoT). In particular, with the advent of sophisticated deep and machine learning-based techniques, this line of research has attracted many researchers to develop more accurate anomaly detection algorithms. The problem itself has been a long-lasting challenging problem in security and especially in malware detection and data tampering. The advancement of the IoT paradigm as well as the increasing number of cyber attacks on the networks of the Internet of Things worldwide raises the concern of whether flexible and simple yet accurate anomaly detection techniques exist. In this paper, we investigate the performance of deep learning-based models including recurrent neural network-based Bidirectional LSTM (BI-LSTM), Long Short-Term Memory (LSTM), CNN-based Temporal Convolutional (TCN), and CuDNN-LSTM, which is a fast LSTM implementation supported by CuDNN. In particular, we assess the performance of these models with respect to accuracy and the training time needed to build such models. According to our experiment, using different timestamps (i.e., 15, 20, and 30 min), we observe that in terms of performance, the CuDNN-LSTM model outperforms other models, whereas in terms of training time, the TCN-based model is trained faster. We report the results of experiments in comparing these four models with various look-back values.

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A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data

Cited by 34 publications

References 14 publications

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

ProtInteract: A deep learning framework for predicting protein–protein interactions

Deep Learning-Based Time-Series Analysis for Detecting Anomalies in Internet of Things

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