Relevant bibliographies by topics / LSTM AutoEncoder

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'LSTM AutoEncoder'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "LSTM AutoEncoder"

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Wu, Sihong, Qinghua Huang, and Li Zhao. "De-noising of transient electromagnetic data based on the long short-term memory-autoencoder." Geophysical Journal International 224, no. 1 (2020): 669–81. http://dx.doi.org/10.1093/gji/ggaa424.

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SUMMARY Late-time transient electromagnetic (TEM) data contain deep subsurface information and are important for resolving deeper electrical structures. However, due to their relatively small signal amplitudes, TEM responses later in time are often dominated by ambient noises. Therefore, noise removal is critical to the application of TEM data in imaging electrical structures at depth. De-noising techniques for TEM data have been developed rapidly in recent years. Although strong efforts have been made to improving the quality of the TEM responses, it is still a challenge to effectively extract the signals due to unpredictable and irregular noises. In this study, we develop a new type of neural network architecture by combining the long short-term memory (LSTM) network with the autoencoder structure to suppress noise in TEM signals. The resulting LSTM-autoencoders yield excellent performance on synthetic data sets including horizontal components of the electric field and vertical component of the magnetic field generated by different sources such as dipole, loop and grounded line sources. The relative errors between the de-noised data sets and the corresponding noise-free transients are below 1% for most of the sampling points. Notable improvement in the resistivity structure inversion result is achieved using the TEM data de-noised by the LSTM-autoencoder in comparison with several widely-used neural networks, especially for later-arriving signals that are important for constraining deeper structures. We demonstrate the effectiveness and general applicability of the LSTM-autoencoder by de-noising experiments using synthetic 1-D and 3-D TEM signals as well as field data sets. The field data from a fixed loop survey using multiple receivers are greatly improved after de-noising by the LSTM-autoencoder, resulting in more consistent inversion models with significantly increased exploration depth. The LSTM-autoencoder is capable of enhancing the quality of the TEM signals at later times, which enables us to better resolve deeper electrical structures.
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Wei, Wangyang, Honghai Wu, and Huadong Ma. "An AutoEncoder and LSTM-Based Traffic Flow Prediction Method." Sensors 19, no. 13 (2019): 2946. http://dx.doi.org/10.3390/s19132946.

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Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.
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Park, Pangun, Piergiuseppe Di Marco, Hyejeon Shin, and Junseong Bang. "Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network." Sensors 19, no. 21 (2019): 4612. http://dx.doi.org/10.3390/s19214612.

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Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.
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Lin, Fei, Yudi Xu, Yang Yang, and Hong Ma. "A Spatial-Temporal Hybrid Model for Short-Term Traffic Prediction." Mathematical Problems in Engineering 2019 (January 14, 2019): 1–12. http://dx.doi.org/10.1155/2019/4858546.

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Accurate and timely short-term traffic prediction is important for Intelligent Transportation System (ITS) to solve the traffic problem. This paper presents a hybrid model called SpAE-LSTM. This model considers the temporal and spatial features of traffic flow and it consists of sparse autoencoder and long short-term memory (LSTM) network based on memory units. Sparse autoencoder extracts the spatial features within the spatial-temporal matrix via full connected layers. It cooperates with the LSTM network to capture the spatial-temporal features of traffic flow evolution and make prediction. To validate the performance of the SpAE-LSTM, we implement it on the real-world traffic data from Qingyang District of Chengdu city, China, and compare it with advanced traffic prediction models, such as models only based on LSTM or SAE. The results demonstrate that the proposed model reduces the mean absolute percent error by more than 15%. The robustness of the proposed model is also validated and the mean absolute percent error on more than 86% road segments is below 20%. This research provides strong evidence suggesting that the proposed SpAE-LSTM effectively captures the spatial-temporal features of the traffic flow and achieves promising results.
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Cai, Jianxian, Xun Dai, Li Hong, Zhitao Gao, and Zhongchao Qiu. "An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network." Mathematical Problems in Engineering 2020 (May 15, 2020): 1–12. http://dx.doi.org/10.1155/2020/3507197.

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Aiming at remedying the problem of low prediction accuracy of existing air pollutant prediction models, a denoising autoencoder deep network (DAEDN) model that is based on long short-term memory (LSTM) networks was designed. This model created a noise reduction autoencoder with an LSTM network to extract the inherent air quality characteristics of original monitoring data and to implement noise reduction processing on monitoring data to improve the accuracy of air quality predictions. The LSTM network structure in the DAEDN model was designed as bidirectional LSTM (Bi-LSTM) to solve the problem of a lag in the unidirectional LSTM prediction results and thereby to further improve the prediction accuracy of the prediction model. Using air pollutant time series data, the DAEDN model was trained using hourly PM2.5 concentration data collected in Beijing over 5 years. The experimental results show that the DAEDN model can extract more stable features from the noisy input after training was completed. The models were evaluated using RMSE and MAE, and the results show that the indexes are 15.504 and 6.789; compared with unidirectional LSTM, it is reduced by 7.33% and 5.87%, respectively. In addition, the new prediction model essentially considered the time series properties of the prediction of the concentration of spatial pollutants and the fully integrated environmental big data, such as air quality monitoring, meteorological monitoring, and forecasting.
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Rákos, Olivér, Szilárd Aradi, Tamás Bécsi, and Zsolt Szalay. "Compression of Vehicle Trajectories with a Variational Autoencoder." Applied Sciences 10, no. 19 (2020): 6739. http://dx.doi.org/10.3390/app10196739.

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The perception and prediction of the surrounding vehicles’ trajectories play a significant role in designing safe and optimal control strategies for connected and automated vehicles. The compression of trajectory data and the drivers’ strategic behavior’s classification is essential to communicate in vehicular ad-hoc networks (VANETs). This paper presents a Variational Autoencoder (VAE) solution to solve the compression problem, and as an added benefit, it also provides classification information. The input is the time series of vehicle positions along actual real-world trajectories obtained from a dataset containing highway measurements, which also serves as the target. During training, the autoencoder learns to compress and decompress this data and produces a small, few element context vector that can represent vehicle behavior in a probabilistic manner. The experiments show how the size of this context vector affects the performance of the method. The method is compared to other approaches, namely, Bidirectional LSTM Autoencoder and Sparse Convolutional Autoencoder. According to the results, the Sparse Autoencoder fails to converge to the target for the specific tasks. The Bidirectional LSTM Autoencoder could provide the same performance as the VAE, though only with double context vector length, proving that the compression capability of the VAE is better. The Support Vector Machine method is used to prove that the context vector can be used for maneuver classification for lane changing behavior. The utilization of this method, considering neighboring vehicles, can be extended for maneuver prediction using a wider, more complex network structure.
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Mallak, Ahlam, and Madjid Fathi. "Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers." Sensors 21, no. 2 (2021): 433. http://dx.doi.org/10.3390/s21020433.

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Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications following the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only are the machines and their components prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig was thoroughly achieved. First, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies, to perform two separate stages of fault detection and diagnosis. The two phases were condensed by—the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework was applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of related work from the past decade, for autoencoders related fault detection and diagnosis in hydraulic systems, was successfully conducted in this study.
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Mallak, Ahlam, and Madjid Fathi. "Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers." Sensors 21, no. 2 (2021): 433. http://dx.doi.org/10.3390/s21020433.

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Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications following the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only are the machines and their components prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig was thoroughly achieved. First, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies, to perform two separate stages of fault detection and diagnosis. The two phases were condensed by—the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework was applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of related work from the past decade, for autoencoders related fault detection and diagnosis in hydraulic systems, was successfully conducted in this study.
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Wu, Ji-Yan, Min Wu, Zhenghua Chen, Xiao-Li Li, and Ruqiang Yan. "Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–10. http://dx.doi.org/10.1109/tim.2021.3055788.

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Park, Kyungnam, Jaeik Jeong, Dongjoo Kim, and Hongseok Kim. "Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM." IEEE Access 8 (2020): 206039–48. http://dx.doi.org/10.1109/access.2020.3036885.

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Dissertations / Theses on the topic "LSTM AutoEncoder"

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Wolpher, Maxim. "Anomaly Detection in Unstructured Time Series Datausing an LSTM Autoencoder." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231368.

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An exploration of anomaly detection. Much work has been done on the topic of anomalyd etection, but what seems to be lacking is a dive into anomaly detection of unstructuredand unlabeled data. This thesis aims to determine the efctiveness of combining recurrentneural networks with autoencoder structures for sequential anomaly detection. The use of an LSTM autoencoder will be detailed, but along the way there will also be backgroundon time-independent anomaly detection using Isolation Forests and Replicator Neural Networks on the benchmark DARPA dataset. The empirical results in this thesis show that Isolation Forests and Replicator Neural Networks both reach an F1-score of 0.98. The RNN reached a ROC AUC score of 0.90 while the Isolation Forest reached a ROC AUC of 0.99. The results for the LSTM Autoencoder show that with 137 features extracted from the unstructured data, it can reach an F1 score of 0.8 and a ROC AUC score of 0.86<br>En undersökning av anomalitetsdetektering. Mycket arbete har gjorts inom ämnet anomalitetsdetektering, men det som verkar saknas är en fördjupning i anomalitetsdetektering av ostrukturerad och omärktdata. Denna avhandling syftar till att bestämma effektiviteten av att kombinera Recurrent Neural Networks med Autoencoder strukturer för sekventiell anomalitetsdetektion. Användningen av en LSTM autoencoder kommeratt beskrivas i detalj, men bakgrund till tidsoberoende anomalitetsdetektering med hjälp av Isolation Forests och Replicator Neural Networks på referens DARPA dataset kommer också att täckas. De empiriska resultaten i denna avhandling visar att Isolation Forestsoch Replicator Neural Networks (RNN) båda når en F1-score på 0,98. RNN nådde en ROC AUC-score på 0,90 medan Isolation Forest nådde en ROC-AUC på 0,99. Resultaten för LSTM Autoencoder visar att med 137 features extraherade från ostrukturerad data kan den nå en F1-score på 0,80 och en ROC AUC-score på 0,86.
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Farahani, Marzieh. "Anomaly Detection on Gas Turbine Time-series’ Data Using Deep LSTM-Autoencoder." Thesis, Umeå universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-179863.

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Anomaly detection with the aim of identifying outliers plays a very important role in various applications (e.g., online spam, manufacturing, finance etc.). An automatic and reliable anomaly detection tool with accurate prediction is essential in many domains. This thesis proposes an anomaly detection method by applying deep LSTM (long short-term memory) especially on time-series data. By validating on real-worlddata at Siemens Industrial Turbomachinery (SIT), the proposed methods hows promising performance, and can be employed in different data domains like device logs of turbine machines to provide useful information on abnormal behaviors. In detail, our proposed method applies an auto encoder to have feature selection by keeping vital features, and learn the time series’s encoded representation. This approach reduces the extensive input data by pulling out the auto encoder’s latent layer output. For prediction, we then train a deep LSTM model with three hidden layers based on the encoder’s latent layer output. Afterwards, given the output from the prediction model, we detect the anomaly sensors related to the specific gas turbine by using a threshold approach. Our experimental results show that our proposed methods perform well on noisy and real-world data set in order to detect anomalies. Moreover, it confirmed that making predictions based on encoding representation, which is under reduction, is more accurate. We could say applying autoencoder can improve both anomaly detection and prediction tasks. Additionally, the performance of deep neural networks would be significantly improved for data with high complexity.
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Ding, Sheng. "A Detachable LSTM with Residual-Autoencoder Features Method for Motion Recognition in Video Sequences." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu160673417735023.

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Blanco, Martínez Alejandro. "Study and design of classification algorithms for diagnosis and prognosis of failures in wind turbines from SCADA data." Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2018. http://hdl.handle.net/10803/586097.

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Actualmente las operaciones de mantenimiento preventivo de los parques eólicos se soportan sobre técnicas de Machine Learning para reducir los costes de las paradas no planificadas. Por eso se necesita una predicción de fallos con cierta anticipación que funcione sobre los datos de SCADA. Estos datos necesitan ser procesados en distintas etapas descritas en esta tesis, con resultados publicados en cada una de ellas. En una primera fase se limpian los valores extremos (Outliers), indicando cómo deben ser tratados para no eliminar la información sobre los fallos. En una segunda, las distintas variables son seleccionadas por diversos métodos de selección de características (Feature Selection). En la misma fase, se compara el uso de variables transformadas mediante Autoencoders. En una tercera se construye el modelo, mediante métodos supervisados y no supervisados, obteniendo resultados destacables con Self Organizing Maps (SOM) y con técnicas de Deep Learning incluyendo redes ANN y LSTM multicapa.<br>Nowadays, the preventive maintenance operations of wind farms are supported by Machine Learning techniques to reduce the costs of unplanned downtime. That is why an early fault prediction that works with SCADA data is required. These data need to be processed at different stages described in this thesis, with results published in each of them. In a first phase, the extreme values (Outliers) are cleaned, indicating how they should address in order not to eliminate the information about the faults. In a second step, the different variables are selected by different Feature Selection methods. At the same step, the use of variables transformed by Autoencoders is also compared. In a third, the model is constructed using Supervised and Unsupervised methods, obtaining outstanding results with Self Organizing Maps (SOM) and Deep Learning techniques including ANN and LSTM multi-layer networks.<br>Actualment les operacions de manteniment preventiu dels parcs eòlics se suporten sobre tècniques de Machine Learning per a reduir els costos de les parades no planificades. Per això es necessita una predicció de fallades amb certa anticipació que funcioni sobre les dades de SCADA. Aquestes dades necessiten ser processades en diferents etapes descrites a aquesta tesi, amb resultats publicats en cadascuna d'elles. En una primera fase es netegen els valors extrems (Outliers), indicant com han de ser tractats per no eliminar la informació sobre les fallades. En una segona, les diferents variables són seleccionades per diversos mètodes de selecció de característiques (Feature Selection). En la mateixa fase, es compara l'ús de variables transformades mitjançant Autoencoders. En una tercera es construeix el model, mitjançant mètodes supervisats i no supervisats, obtenint resultats destacables amb Self Organizing Maps (SOM) i amb tècniques de Deep Learning incloent xarxes ANN i LSTM multicapa.
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Lousseief, Elias. "MahlerNet : Unbounded Orchestral Music with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264993.

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Modelling music with mathematical and statistical methods in general, and with neural networks in particular, has a long history and has been well explored in the last decades. Exactly when the first attempt at strictly systematic music took place is hard to say; some would say in the days of Mozart, others would say even earlier, but it is safe to say that the field of algorithmic composition has a long history. Even though composers have always had structure and rules as part of the writing process, implicitly or explicitly, following rules at a stricter level was well investigated in the middle of the 20th century at which point also the first music writing computer program based on mathematics was implemented. This work in computer science focuses on the history of musical composition with computers, also known as algorithmic composition, using machine learning and neural networks and consists of two parts: a literature survey covering in-depth the last decades in the field from which is drawn inspiration and experience to construct MahlerNet, a neural network based on the previous architectures MusicVAE, BALSTM, PerformanceRNN and BachProp, capable of modelling polyphonic symbolic music with up to 23 instruments. MahlerNet is a new architecture that uses a custom preprocessor with musical heuristics to normalize and filter the input and output files in MIDI format into a data representation that it uses for processing. MahlerNet, and its preprocessor, was written altogether for this project and produces music that clearly shows musical characteristics reminiscent of the data it was trained on, with some long-term structure, albeit not in the form of motives and themes.<br>Matematik och statistik i allmänhet, och maskininlärning och neurala nätverk i synnerhet, har sedan långt tillbaka använts för att modellera musik med en utveckling som kulminerat under de senaste decennierna. Exakt vid vilken historisk tidpunkt som musikalisk komposition för första gången tillämpades med strikt systematiska regler är svårt att säga; vissa skulle hävda att det skedde under Mozarts dagar, andra att det skedde redan långt tidigare. Oavsett vilket, innebär det att systematisk komposition är en företeelse med lång historia. Även om kompositörer i alla tider följt strukturer och regler, medvetet eller ej, som en del av kompositionsprocessen började man under 1900-talets mitt att göra detta i högre utsträckning och det var också då som de första programmen för musikalisk komposition, baserade på matematik, kom till. Den här uppsatsen i datateknik behandlar hur musik historiskt har komponerats med hjälp av datorer, ett område som också är känt som algoritmisk komposition. Uppsatsens fokus ligger på användning av maskininlärning och neurala nätverk och består av två delar: en litteraturstudie som i hög detalj behandlar utvecklingen under de senaste decennierna från vilken tas inspiration och erfarenheter för att konstruera MahlerNet, ett neuralt nätverk baserat på de tidigare modellerna MusicVAE, BALSTM, PerformanceRNN och BachProp. MahlerNet kan modellera polyfon musik med upp till 23 instrument och är en ny arkitektur som kommer tillsammans med en egen preprocessor som använder heuristiker från musikteori för att normalisera och filtrera data i MIDI-format till en intern representation. MahlerNet, och dess preprocessor, är helt och hållet implementerade för detta arbete och kan komponera musik som tydligt uppvisar egenskaper från den musik som nätverket tränats på. En viss kontinuitet finns i den skapade musiken även om det inte är i form av konkreta teman och motiv.
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Tomašov, Adrián. "Analýza GPON rámců s využitím strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413085.

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Táto práca sa zameriava na analýzu vybraných častí GPON rámca pomocou algoritmov strojového učenia implementovaných pomocou knižnice TensorFlow. Vzhľadom na to, že GPON protokol je definovaný ako sada odporúčaní, implementácia naprieč spoločnosťami sa môže líšiť od navrhnutého protokolu. Preto analýza pomocou zásobníkového automatu nie je dostatočná. Hlavnou myšlienkou je vytvoriť systém modelov za použitia knižnice TensorFlow v Python3, ktoré sú schopné detekovať abnormality v komunikácií. Tieto modely používajú viaceré architektúry neuronových sietí (napr. LSTM, autoencoder) a zameriavajú sa na rôzne typy analýzy. Tento systém sa naučí na vzorovej vzorke dát a upozorní na nájdené odlišnosti v novozachytenej komunikácií. Výstupom systému odhad podobnosti aktuálnej komunikácie v porovnaní so vzorovou komunikáciou.
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Golshan, Arman. "A contemporary machine learning approach to detect transportation mode - A case study of Borlänge, Sweden." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-35966.

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Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.
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Natvig, Filip. "Knowledge Transfer Applied on an Anomaly Detection Problem Using Financial Data." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451884.

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Anomaly detection in high-dimensional financial transaction data is challenging and resource-intensive, particularly when the dataset is unlabeled. Sometimes, one can alleviate the computational cost and improve the results by utilizing a pre-trained model, provided that the features learned from the pre-training are useful for learning the second task. Investigating this issue was the main purpose of this thesis. More specifically, it was to explore the potential gain of pre-training a detection model on one trader's transaction history and then retraining the model to detect anomalous trades in another trader's transaction history. In the context of transfer learning, the pre-trained and the retrained model are usually referred to as the source model and target model, respectively.  A deep LSTM autoencoder was proposed as the source model due to its advantages when dealing with sequential data, such as financial transaction data. Moreover, to test its anomaly detection ability despite the lack of labeled true anomalies, synthetic anomalies were generated and included in the test set. Various experiments confirmed that the source model learned to detect synthetic anomalies with highly distinctive features. Nevertheless, it is hard to draw any conclusions regarding its anomaly detection performance due to the lack of labeled true anomalies. While the same is true for the target model, it is still possible to achieve the thesis's primary goal by comparing a pre-trained model with an identical untrained model. All in all, the results suggest that transfer learning offers a significant advantage over traditional machine learning in this context.
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Åkerström, Emelie. "Real-time Outlier Detection using Unbounded Data Streaming and Machine Learning." Thesis, Luleå tekniska universitet, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80044.

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Accelerated advancements in technology, the Internet of Things, and cloud computing have spurred an emergence of unstructured data that is contributing to rapid growth in data volumes. No human can manage to keep up with monitoring and analyzing these unbounded data streams and thus predictive and analytic tools are needed. By leveraging machine learning this data can be converted into insights which are enabling datadriven decisions that can drastically accelerate innovation, improve user experience, and drive operational efficiency. The purpose of this thesis is to design and implement a system for real-time outlier detection using unbounded data streams and machine learning. Traditionally, this is accomplished by using alarm-thresholds on important system metrics. Yet, a static threshold cannot account for changes in trends and seasonality, changes in the system, or an increased system load. Thus, the intention is to leverage machine learning to instead look for deviations in the behavior of the data not caused by natural changes but by malfunctions. The use-case driving the thesis forward is real-time outlier detection in a Content Delivery Network (CDN). The input data includes Http-error messages received by clients, and contextual information like region, cache domains, and error codes, to provide tailormade predictions accounting for the trends in the data. The outlier detection system consists of a data collection pipeline leveraging the technique of stream processing, a MiniBatchKMeans clustering model that provides online clustering of incoming data according to their similar characteristics, and an LSTM AutoEncoder that accounts for temporal nature of the data and detects outlier data points in the clusters. An important finding is that an outlier is defined as an abnormal amount of outlier data points all originating from the same cluster, not a single outlier data point. Thus, the alerting system will be implementing an outlier percentage threshold. The experimental results show that an outlier is detected within one minute from a cache break-down. This triggers an alert to the system owners, containing graphs of the clustered data to narrow down the search area of the cause to enable preventive action towards the prominent incident. Further results show that within 2 minutes from fixing the cause the system will provide feedback that the actions taken were successful. Considering the real-time requirements of the CDN environment, it is concluded that the short delay for detection is indeed real-time. Proving that machine learning is indeed able to detect outliers in unbounded data streams in a real-time manner. Further analysis shows that the system is more accurate during peakhours when more data is in circulation than during none peak-hours, despite the temporal LSTM layers. Presumably, an effect from the model needing to train on more data to better account for seasonality and trends. Future work necessary to put the outlier detection system in production thus includes more training to improve accuracy and correctness. Furthermore, one could consider implementing necessary functionality for a production environment and possibly adding enhancing features that can automatically avert incidents detected and handle the causes of them.
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Berenji, Ardestani Sarah. "Time Series Anomaly Detection and Uncertainty Estimation using LSTM Autoencoders." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281354.

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The goal of this thesis is to implement an anomaly detection tool using LSTM autoencoder and apply a novel method for uncertainty estimation using Bayesian NeuralNetworks (BNNs) based on a paper from Uber research group [1]. Having a reliable anomaly detection tool and accurate uncertainty estimation is critical in many fields. At Telia, such a tool can be used in many different data domains like device logs to detect abnormal behaviours. Our method uses an autoencoder to extract important features and learn the encoded representation of the time series. This approach helps to capture testing data points coming from a different population. We then train a prediction model based on this encoder’s representation of data. An uncertainty estimation algorithm is used to estimate the model’s uncertainty, which breaks it down to three different sources: model uncertainty, model misspecification, and inherent noise. To get the first two, a Monte Carlo dropout approach is used which is simple to implement and easy to scale. For the third part, a bootstrap approach that estimates the noise level via the residual sum of squares on validation data is used. As a result, we could see that our proposed model can make a better prediction in comparison to our benchmarks. Although the difference is not big, yet it shows that making prediction based on encoding representation is more accurate. The anomaly detection results based on these predictions also show that our proposed model has a better performance than the benchmarks. This means that using autoencoder can improve both prediction and anomaly detection tasks. Additionally, we conclude that using deep neutral networks would show bigger improvement if the data has more complexity.<br>Målet med den här uppsatsen är att implentera ett verktyg för anomaliupptäckande med hjälp av LSTM autoencoders och applicera en ny metod för osäkerhetsestimering med hjälp av Bayesian Neural Networks (BNN) baserat på en artikel från Uber research group [1]. Pålitliga verktyg för att upptäcka anomalier och att göra precisa osäkerhetsestimeringar är kritiskt i många fält. På Telia kan ett sådant verktyg användas för många olika datadomäner, som i enhetsloggar för att upptäcka abnormalt beteende. Vår metod använder en autoencoder för att extrahera viktiga egenskaper och lära sig den kodade representationen av tidsserierna. Detta tillvägagångssätt hjälper till med att ta in testdatapunker som kommer in från olika grundmängder. Sedan tränas en förutsägelsemodell baserad på encoderns representation av datan. För att uppskatta modellens osäkerhet används en uppskattningsalgoritm som delar upp osäkerheten till tre olika källor. Dessa tre källor är: modellosäkerhet, felspeciferad model, och naturligt brus. För att få de första två används en Monte Carlo dropout approach som är lätt att implementera och enkel att skala. För den tredje delen används en enkel anfallsvikel som uppskattar brusnivån med hjälp av felkvadratsumman av valideringsdatan. Som ett resultat kunde vi se att vår föreslagna model kan göra bättre förutsägelser än våra benchmarks. Även om skillnaden inte är stor så visar det att att använda autoencoderrepresentation för att göra förutsägelser är mer noggrant. Resulaten för anomaliupptäckanden baserat på dessa förutsägelser visar också att vår föreslagna modell har bättre prestanda än benchmarken. Det betyder att användning av autoencoders kan förbättra både förutsägelser och anomaliupptäckande. Utöver det kan vi dra slutsatsen att användning av djupa neurala nätverk skulle visa en större förbättring om datan hade mer komplexitet.
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Book chapters on the topic "LSTM AutoEncoder"

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He, Jie, Xingjiao Wu, Wenxin Hu, and Jing Yang. "LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencoder Framework." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_12.

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Xiao, Hui, Donghai Guan, Rui Zhao, Weiwei Yuan, Yaofeng Tu, and Asad Masood Khattak. "Semi-supervised Time Series Anomaly Detection Model Based on LSTM Autoencoder." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3150-4_4.

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Vavra, Jan, and Martin Hromada. "Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment." In Intelligent Systems Applications in Software Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30329-7_28.

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Fukuda, Kiyohito, Naoki Mori, and Keinosuke Matsumoto. "A Novel Sentence Vector Generation Method Based on Autoencoder and Bi-directional LSTM." In Distributed Computing and Artificial Intelligence, 15th International Conference. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94649-8_16.

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Zhu, Dong, Chengkun Wu, Chuanfu Xu, and Zhenghua Wang. "AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_24.

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Chouliaras, Spyridon, and Stelios Sotiriadis. "Detecting Performance Degradation in Cloud Systems Using LSTM Autoencoders." In Advanced Information Networking and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75075-6_38.

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Nalbach, Oliver, Sebastian Bauer, Nanna Dahlem, and Dirk Werth. "Real-Time Detection of Unusual Customer Behavior in Retail Using LSTM Autoencoders." In Business Information Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53337-3_7.

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Hahner, Sara, Rodrigo Iza-Teran, and Jochen Garcke. "Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM Autoencoders." In Artificial Neural Networks and Machine Learning – ICANN 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61609-0_23.

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Coto-Jiménez, Marvin, John Goddard-Close, and Fabiola Martínez-Licona. "Improving Automatic Speech Recognition Containing Additive Noise Using Deep Denoising Autoencoders of LSTM Networks." In Speech and Computer. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_42.

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Kotenko, Igor, Oleg Lauta, Kseniya Kribel, and Igor Saenko. "LSTM Neural Networks for Detecting Anomalies Caused by Web Application Cyber Attacks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210014.

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Detecting anomalies in the traffic of computer networks is an important step in protecting and countering various types of cyber attacks. Among the many methods and approaches for detecting anomalies in network traffic, the most popular are machine learning methods that allow one to achieve high accuracy with minimal errors. One of the ways to improve the efficiency of anomaly detection using machine learning is the use of artificial neural networks of complex architecture, in particular, networks with long short-term memory (LSTM), which have demonstrated high efficiency in many areas. The paper is devoted to the study of the capabilities of LSTM neural networks for detecting network anomalies. It proposes using LSTM neural networks to detect network anomalies caused by cyber attacks to bypass Web Application Firewall vulnerabilities that are very difficult to detect by other means. For this purpose, it is proposed to use LSTM in conjunction with an autoencoder. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirmed the high efficiency of the developed approach. Experiments have shown that the proposed approach allows detecting cyber attacks in real or near real time.
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Conference papers on the topic "LSTM AutoEncoder"

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Said Elsayed, Mahmoud, Nhien-An Le-Khac, Soumyabrata Dev, and Anca Delia Jurcut. "Network Anomaly Detection Using LSTM Based Autoencoder." In MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, 2020. http://dx.doi.org/10.1145/3416013.3426457.

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Chen, Mu-Yen, Tien-Chi Huang, Yu Shu, Chia-Chen Chen, Tsung-Che Hsieh, and Neil Y. Yen. "Learning the Chinese Sentence Representation with LSTM Autoencoder." In Companion of the The Web Conference 2018. ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186355.

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Noguchi, Wataru, Hiroyuki Iizuka, and Masahito Yamamoto. "Proposing Multimodal Integration Model Using LSTM and Autoencoder." In 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2016. http://dx.doi.org/10.4108/eai.3-12-2015.2262505.

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Huang, Kun-Yi, Chung-Hsien Wu, Tsung-Hsien Yang, Ming-Hsiang Su, and Jia-Hui Chou. "Speech emotion recognition using autoencoder bottleneck features and LSTM." In 2016 International Conference on Orange Technologies (ICOT). IEEE, 2016. http://dx.doi.org/10.1109/icot.2016.8278965.

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Paul, Sudipta, and Subhankar Mishra. "LAC: LSTM AUTOENCODER with Community for Insider Threat Detection." In ICBDR 2020: 2020 the 4th International Conference on Big Data Research. ACM, 2020. http://dx.doi.org/10.1145/3445945.3445958.

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Van Hoa, Tran, Duong Tuan Anh, and Duong Ngoc Hieu. "Foreign Exchange Rate Forecasting using Autoencoder and LSTM Networks." In ICIIT '21: 2021 6th International Conference on Intelligent Information Technology. ACM, 2021. http://dx.doi.org/10.1145/3460179.3460184.

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Liu, Yuyu. "Forecast of photovoltaic power generation using deep-learning algorithms: evaluation of LSTM, LSTM-autoencoder, and LSTM-attention-mechanism." In Physics, Simulation, and Photonic Engineering of Photovoltaic Devices X, edited by Alexandre Freundlich, Karin Hinzer, and Stéphane Collin. SPIE, 2021. http://dx.doi.org/10.1117/12.2583409.

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Oota, Subba Reddy, Vijay Rowtula, Manish Gupta, and Raju S. Bapi. "StepEncog: A Convolutional LSTM Autoencoder for Near-Perfect fMRI Encoding." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852339.

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Zhao, Xia, Xiao Han, Weijun Su, and Zhen Yan. "Time series prediction method based on Convolutional Autoencoder and LSTM." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996842.

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Xu, Zhenyi, Yu Kang, Yang Cao, and Longchuan Yue. "Residual Autoencoder-LSTM for City Region Vehicle Emission Pollution Prediction." In 2018 IEEE 14th International Conference on Control and Automation (ICCA). IEEE, 2018. http://dx.doi.org/10.1109/icca.2018.8444183.

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