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Multi-step-ahead Prediction With long short term memory Networks and Support Vector Regression

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Multi-step-ahead Prediction With Long Short Term Memory Netw...

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

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作者： Sai Li Huajing Fang Bing Shi School of Automation National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology

long-term machine condition prognosis is very important for condition-based maintenance. With the rapid development of deep learning, we use long short term memory（LSTM） network with error correction based on support vector regression（SVR）to realize multi-step-ahead time series prediction in this paper. As the parameters of trained LSTM model cannot be updated in the forecasting stage, the SVR model is used to predict the evolution of the residual error in LSTM to update the model parameters. The proposed LSTM-SVR model is validated and compared with LSTM and SVR model using Mackey-Glass time series and TE benchmark data. The experimental results show the proposed method is effective and can achieve a considerable multi-step-ahead forecasting performance.

关键词： long short term memory Multi-step-ahead Prediction Support Vector Regression Error Correction

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Tsinghua Science and Technology 2024年第2期29卷 356-367页

作者： Xing Wang Ling Wang Chenxin Dong Hao Ren Ke Xing Department of Automation Tsinghua UniversityBeijing 100080China School of Mechanical and Automotive Engineering Qingdao Hengxing University of Science and TechnologyQingdao 266100China Meituan Beijing 100015China

On-demand food delivery(OFD)is gaining more and more popularity in modern *** a kernel order assignment manner in OFD scenario,order recommendation directly influences the delivery efficiency of the platform and the delivery experience of *** paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution *** actor-critic network based on long short term memory(LSTM)unit is designed to deal with the order-grabbing conflict between different ***,three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different *** test the performance of the proposed method,extensive experiments are conducted based on real data from Meituan delivery *** results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts,resulting in better delivery efficiency and experience for the platform and riders.

关键词： on-demand food delivery order recommendation reinforcement learning actor-critic network long short term memory

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

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Digital Communications and Networks 2022年第4期8卷 531-539页

作者： Ke Yan Xiaokang Zhou Department of the Built Environment National University of Singapore4 Architecture Drive117566Singapore Faculty of Data Science Shiga UniversityHikone5228522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo1030027Japan

Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’*** the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly *** studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy *** the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various ***,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification *** this study,we propose to perform chiller FDD using a CNN-based *** proposed approach has two distinct advantages over existing machine learning-based chiller AFDD ***,the CNN-based approach does not require the feature selection/extraction *** CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional ***,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.

关键词： Chiller Fault detection and diagnosis Deep learning neural network long short term memory Recurrent neural network Gated recurrent unit

Application of deep learning technique to the sea surface height prediction in the South China Sea

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Acta Oceanologica Sinica 2021年第7期40卷 68-76页

作者： Tao Song Ningsheng Han Yuhang Zhu Zhongwei Li Yineng Li Shaotian Li Shiqiu Peng College of Computer and Communication Engineering China University of Petroleum(East China)Qingdao 266580China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou 511458China Key Laboratory of Science and Technology on Operational Oceanography Chinese Academy of SciencesGuangzhou 511458China Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf Bubei Gulf UniversityQinzhou 535011China Department of Artificial Intelligence Faculty of Computer SciencePolytechnical University of MadridBoadilla del Monte 28660MadridSpain

A deep-learning-based method,called ConvLSTMP3,is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs,in which the spatial features are“learned”by convolutional operations while the temporal features are tracked by long short term memory(LSTM).Trained by a reanalysis dataset of the South China Sea(SCS),ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in *** results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4%averaged over a 15-d prediction *** particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean *** the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction,and could be an alternative way in the operational prediction for ocean environments in the future.

关键词： deep learning sea surface height prediction convolutional operation long short term memory

A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

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Chinese Journal of Chemical Engineering 2023年第3期55卷 266-276页

作者： Yiming Bai Shuaiyu Xiang Feifan Cheng Jinsong Zhao Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China Sinopec Engineering Incorporation Beijing 100084China

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

关键词： Fault prognosis Process systems Safety Prediction Principal component analysis long short term memory

Bitcoin Candlestick Prediction with Deep Neural Networks Based on Real Time Data

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Computers, Materials & Continua 2021年第9期68卷 3215-3233页

作者： Reem K.Alkhodhairi Shahad R.Aljalhami Norah K.Rusayni Jowharah F.Alshobaili Amal A.Al-Shargabi Abdulatif Alabdulatif Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia

Currently,Bitcoin is the world’s most popular *** price of Bitcoin is extremely volatile,which can be described as high-benefit and *** minimize the risk involved,a means of more accurately predicting the Bitcoin price is *** of the existing studies of Bitcoin prediction are based on historical(i.e.,benchmark)data,without considering the real-time(i.e.,live)*** mitigate the issue of price volatility and achieve more precise outcomes,this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open,high,low,and close(OHLC)—*** a better prediction model,the present study proposes time series-based deep learning *** particular,two deep learning algorithms were applied,namely,long short-term memory(LSTM)and gated recurrent unit(GRU).Using real-time data,the Bitcoin candlesticks were predicted for three intervals:the next 4 h,the next 12 h,and the next 24 *** results showed that the best-performing model was the LSTM-based model with the 4-h *** particular,this model achieved a stellar performance with a mean absolute percentage error(MAPE)of 0.63,a root mean square error(RMSE)of 0.0009,a mean square error(MSE)of 9e-07,a mean absolute error(MAE)of 0.0005,and an R-squared coefficient(R2)of *** these results,the proposed prediction model has demonstrated its efficiency over the models proposed in previous *** findings of this study have considerable implications in the business field,as the proposed model can assist investors and traders in precisely identifying Bitcoin sales and buying opportunities.

关键词： Bitcoin prediction long short term memory gated recurrent unit deep neural networks real-time data

Roman Urdu News Headline Classification Empowered with Machine Learning

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Computers, Materials & Continua 2020年第11期65卷 1221-1236页

作者： Rizwan Ali Naqvi Muhammad Adnan Khan Nauman Malik Shazia Saqib Tahir Alyas Dildar Hussain Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computational Sciences Korea Institute for Advanced StudySeoul02455Korea

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak *** from the subcontinent may speak the same Urdu language but they might be using different scripts for *** communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information *** Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the *** is due to the numerous complexities involved in the processing of Roman Urdu *** complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured *** have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be *** author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different *** preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the *** applying above mentioned different machine learnin

关键词： Roman urdu news headline classification long short term memory recurrent neural network logistic regression multinomial naïve Bayes random forest k neighbor gradient boosting classifier

short term Traffic Flow Prediction Using Hybrid Deep Learning

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Computers, Materials & Continua 2023年第4期75卷 1641-1656页

作者： Mohandu Anjaneyulu Mohan Kubendiran School of Computer Science and Engineering(SCOPE) Vellore Institute of Technology(VIT)Vellore632014India

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). short term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), long short term memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential ***, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error *** extensive experimental analysis was performed on the benchmark *** evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

关键词： short term traffic flow prediction principal component analysis stacked auto encoders long short term memory k nearest neighbors:intelligent transportation system

Deep learning for the internet of things:Potential benefits and use-cases

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Digital Communications and Networks 2021年第4期7卷 526-542页

作者： Tausifa Jan Saleem Mohammad Ahsan Chishti Department of Computer Science and Engineering National Institute of TechnologySrinagarJ&KIndia

The massive number of sensors deployed in the Internet of Things(IoT)produce gigantic amounts of data for facilitating a wide range of *** Learning(DL)would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter *** this regard,exploring the potential of DL for IoT data analytics becomes highly *** paper begins with a concise discussion on the Deep Neural Network(DNN)and its different *** potential benefits that DL will bring to the IoT are also ***,a detailed review of DL-driven IoT use-cases is ***,this paper formulates a DL-based model for Human Activity Recognition(HAR).It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other *** from enlightening the potential of DL in IoT applications,this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.

关键词： Internet of things(IoT) Deep learning Convolutional neural network Recurrent neural network long short term memory