Singular Point Probability Improve LSTM Network Performance for Long-term Traffic Flow Prediction

Liu, Boyi; Cheng, Jieren; Cai, Kuanqi; Shi, Pengchao; Tang, Xiangyan

doi:10.1007/978-981-10-6893-5_24

Cited by 25 publications

(8 citation statements)

References 17 publications

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“…The traffic flow prediction algorithm described above has been reported and published orally at the 2017 National Conference of Theoretical Computer Science of China [29]. After discussion, we need to improve in the following directions.…”

Section: Introductionmentioning

confidence: 99%

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Liu¹,

Cheng²,

Liu³

et al. 2018

Preprint

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Accurate and fast traffic flow forecasting is vital in intelligent transportation system because many of the advanced features in intelligent transportation systems are based on it. However, existing methods have poor performance regarding accuracy and computational efficiency in long-term traffic flow forecasting under big data. Hence, we propose an improved Long short-term memory (LSTM) Network and its cluster computing implementation in this paper to address the above challenge. We propose a singular point probability LSTM (SDLSTM) algorithm. The method discards the units of the network according to the singular point probability during the training process and amends the SDLSTM by Autoregressive Integrated Moving Average Model (ARIMA) to achieve the accurate prediction of 24-hour traffic flow data. Furthermore, the paper designs a scheme for implementing this method through cluster computing to shorten the calculation time and improve the system's operating speed. Theoretical analysis and experimental results show that SDLSTM gains a higher accuracy rate and better stability in the long-term traffic flow forecasting compared with previous methods.

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Section: Introductionmentioning

confidence: 99%

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Liu¹,

Cheng²,

Liu³

et al. 2018

Preprint

Self Cite

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“…In We believe that the approach will have great value in the security of cloud computing, cloud robotics [56], intelligent transportation [57], IOT and so on.…”

Section: Resultsmentioning

confidence: 99%

Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

Cheng¹,

Zhang

Tang

et al. 2018

Security and Communication Networks

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Correspondence should be addressed to Chen Zhang; 314848554@qq.com.Distributed denial of service (DDoS) attacks have caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the inter-class mean with a gradient ascent and reducing the intra-class variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple kernel learning (SMKL) models with two characteristics including inter-class mean squared difference growth (M-SMKL) and intra-class variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

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“…Overall, our research contributes significantly to enhancing the accuracy and reliability of PPI forecasting by creative design and realization of the proposed algorithm. With the repaid development of cloud robotics [Liu et al (2019)] and smart cities [Liu et al (2017)], the method will be widely used. The method is especially useful for the development of the economic field.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

Tang¹,

Wang²,

Cheng

et al. 2019

Computers, Materials &Amp; Continua

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The accuracy of predicting the Producer Price Index (PPI) plays an indispensable role in government economic work. However, it is difficult to forecast the PPI. In our research, we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA (Autoregressive Integrated Moving Average Model) models. The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation. The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI, and produced three different sequences of fuzzy information granules, whose Support Vector Regression (SVR) machine forecast models were separately established for their Genetic Algorithm (GA) optimization parameters. Finally, the residual errors of the GA-SVR model were rectified through ARIMA modeling, and the PPI estimate was reached. Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models, including ARIMA, GRNN, and GA-SVR, following several comparative experiments. Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.

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Singular Point Probability Improve LSTM Network Performance for Long-term Traffic Flow Prediction

Cited by 25 publications

References 17 publications

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

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