A Review of Deep Learning Models for Time Series Prediction

Han, Zhongyang; Zhao, Jun; Leung, Henry; Ma, King; Wang, Wei

doi:10.1109/jsen.2019.2923982

Cited by 276 publications

(104 citation statements)

References 170 publications

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“…In [23], a per lane VSL, based on Lagrangian control using Deep-RL (DRL) is proposed. It is a learning structure based on Recurrent Neural Network (RNN) [57] where the application of the so-called Gated Recurrent Unit (GRU) [58] mechanism solves the problem of vanishing gradient (decay of information through time) that occurs when learning a classic RNN using the backpropagation through time procedure. GRU is a simplified version of the Long-Short Term Memory (LSTM) recurrent network introduced in [59] that combines forget and input gates to form an 'update' gate.…”

Section: Results Of Research Questionsmentioning

confidence: 99%

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

et al. 2020

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Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented.

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Section: Results Of Research Questionsmentioning

confidence: 99%

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

et al. 2020

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“…In recent years, flow predictions based on time series have always been an attractive research area. Developing predictive models plays an important role in interpreting complex real-world elements [7].…”

Section: Related Workmentioning

confidence: 99%

“…e advantage of a Gaussian processes lies in its ability of modeling the uncertainty hidden in data, which is provided by predicting distributions [20]. Deep learning-based models are good at discovering intricate structure in large data sets [7]. ese prediction models can well explain the randomness and periodicity of flow.…”

Section: Related Workmentioning

confidence: 99%

A Peak Prediction Method for Subflow in Hybrid Data Flow

Zhang

Liu

Chen

et al. 2020

Scientific Programming

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Subflow prediction is required in resource active elastic scaling, but the existing single flow prediction methods cannot accurately predict the peak variation of subflow in hybrid data flow. These do not consider the correlation between subflows. The difficulty is that it is hard to calculate the correlation between different data flows in hybrid data flow. In order to solve this problem, this paper proposes a new method DCCSPP (subflow peak prediction of hybrid data flow based on delay correlation coefficients) to predict the peak value of hybrid data flow. Firstly, we establish a delay correlation coefficient model based on the sliding time window to determine the delay time and delay correlation coefficient. Next, based on the model, a hybrid data flow subflow peak prediction model and algorithm are established to achieve accurate peak prediction of subflow. Experiments show that our prediction model has achieved better results. Compared with LSTM, our method has decreased the MAE about 18.36% and RMSE 13.50%. Compared with linear regression, MAE and RMSE are decreased by 27.12% and 25.58%, respectively.

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“…Later FFMLP was refined to the radial basis function network using Gaussian as a transfer function (Trajkovic 2005 ), generalized regression neural network (Feng et al 2017b ), extreme learning machine (ELM), and its hybridised variants (Reis et al 2019 ; Zhu et al 2020 ). Recently, deep learning models have gained a great deal of attention among other AI models attributed to their features such as increased accuracy, robustness, efficiency, decreased computational costs, and overall modelling effectiveness (Han et al 2019 ). In the domain of hydrology, especially in ETo modelling, the exploration of deep learning architectures has been sporadic.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R 2 ) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R 2 . The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.

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A Review of Deep Learning Models for Time Series Prediction

Cited by 276 publications

References 170 publications

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

A Peak Prediction Method for Subflow in Hybrid Data Flow

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

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