Long sequence time-series forecasting with deep learning: A survey

Chen, Zonglei; Ma, Minbo; Li, Tianrui; Wang, Hongjun; Li, Chongshou

doi:10.1016/j.inffus.2023.101819

Cited by 76 publications

(10 citation statements)

References 103 publications

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“…Additionally, deep learning algorithms have been extensively utilized in time series forecasting, allowing the accurate prediction of future data points [12,50]. This forecasting approach is effective in tasks such as sensor signal extrapolation and predicting future outputs.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, deep learning networks have emerged as powerful tools for time series forecasting or predicting future data, using techniques such as CNNs, recurrent neural networks (RNNs), and temporal convolutional networks (TCNs) [12]. For example, Pirani [13] explored various RNN-based architectures for financial time series forecasting, demonstrating that models incorporating gated recurrent unit (GRU) layers outperformed other recurrent networks.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

Esmaeili,

Cassie,

Nguyen

et al. 2023

Bioengineering

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Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

Esmaeili,

Cassie,

Nguyen

et al. 2023

Bioengineering

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“…It is widely used in various fields, including transportation. TSA elucidates data behavior by extracting information on trends, seasonality, and residuals from the data [34]. Based on this understanding, predictions and decisions are made.…”

Section: Based On Mathematical and Statistical Modelsmentioning

confidence: 99%

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

Jiang,

Ding,

et al. 2024

Algorithms

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The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

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“…The fusion of attention mechanisms with RNNs marks a substantial advancement in sequence modeling, overcoming limitations inherent in conventional RNN structures. By incorporating attention mechanisms, they gain the ability to selectively focus on different parts of the input sequence, offering a dynamic and context-aware approach to processing sequential data [32], [33].…”

Section: B Rnn With Attentionmentioning

confidence: 99%

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

Golda,

Mekonen,

Pandey

et al. 2024

IEEE Access

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Generative Artificial Intelligence (GAI) has sparked a transformative wave across various domains, including machine learning, healthcare, business, and entertainment, owing to its remarkable ability to generate lifelike data. This comprehensive survey offers a meticulous examination of the privacy and security challenges inherent to GAI. It provides five pivotal perspectives essential for a comprehensive understanding of these intricacies. The paper encompasses discussions on GAI architectures, diverse generative model types, practical applications, and recent advancements within the field. In addition, it highlights current security strategies and proposes sustainable solutions, emphasizing user, developer, institutional, and policymaker involvement.

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Long sequence time-series forecasting with deep learning: A survey

Cited by 76 publications

References 103 publications

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

Privacy and Security Concerns in Generative AI: A Comprehensive Survey

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