A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series

Wang, Rongxi; Peng, Caiyuan; Gao, Jianmin; Gao, Zhiyong; Jiang, Hongquan

doi:10.1007/s40314-019-1006-2

Cited by 25 publications

(13 citation statements)

References 56 publications

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“…Miscellaneous: In recent years, the TCN has been one of the most widely checked general purpose architectures for time series forecasting. 182,189,190,192 However, any of the other network architectures can be applied to time series of miscellaneous application domains not classified in Table 6. For example, CNN and RNN can be used to detect human activity 186 or hybrid models to detect anomalies.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…The majority of current prediction methods used in food safety are single-step prediction or fitting prediction techniques, which cannot predict unknown data, that is, future occurrences. As a significant research area in data analysis, time series forecasting plays an important role in the processing industry [30], clinical medicine [31], and other sectors [32] because of its capability to analyze the historical data of a dynamic system and predict future operation patterns [33]. This feature is consistent with the requirement of food safety risk prediction.…”

Section: Early-warning Models Of Food Safety Riskmentioning

confidence: 73%

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Wang

Zou

et al. 2022

Foods

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Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

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“…For example, CNN was used to optimize LSTM, and significant results have been obtained [14]. The researchers [15] have developed an improved LSTM that can partially predict the trend of time series. However, most of the improved models are based on the LSTM structure, and little attention has been paid to LSTM weight parameters.…”

Section: Motivationmentioning

confidence: 99%

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

et al. 2022

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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

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A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series

Cited by 25 publications

References 56 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

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