LSTM based reserve prediction for bank outlets

Liu, Yu; Dong, Shuting; Lu, Mingming; Wang, Jianxin

doi:10.26599/tst.2018.9010007

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…Hochreiter and Schmidhuber proposed LSTM with memory function [28] . It consists of three parts: the input, forget, and output gates [29] . The input data are judged by whether it is needed in accordance with the algorithm.…”

Section: Cnn and Grumentioning

confidence: 99%

Automatic modulation recognition based on CNN and GRU

Liu

Zhang

Zhou

2022

Tsinghua Sci. Technol.

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Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.

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Section: Cnn and Grumentioning

confidence: 99%

Automatic modulation recognition based on CNN and GRU

Liu

Zhang

Zhou

2022

Tsinghua Sci. Technol.

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“…A long shortterm memory network is a common cyclic neural network; LSTM networks were proposed by Liu et al and were improved and extended by many people in the subsequent work [13,14]. ey are very effective in many problems, and nowadays they are widely used.…”

Section: Development and Structure Of Lstmmentioning

confidence: 99%

Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network

Liang

Cheng

2021

Mobile Information Systems

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Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.

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“…Given the sufficient long-term memory ability of the LSTM, some scholars attempted to employ it for predicting the long time series. Tian et al [32], Liu et al [33], and Wang et al [34] designed the mid-and long-term prediction models for the traffic flow, the reserve requirements of bank outlets, and the earthquakes based on the LSTM, respectively. As a result, they all obtained good prediction accuracy.…”

Section: Prediction Of Web Applicationmentioning

confidence: 99%

“…Owing to the superior long-term memory ability, the LSTM exhibits excellent potential for predicting the long time series. ere have been several good attempts on applying the LSTM to carry out the mid-and long-term prediction for time series, such as traffic flow [32], bank business [33], and earthquakes [34]. Under such background, we choose to employ the LSTM for predicting the future cycle workload of web applications and evaluate its prediction performance by comparing with other popular approaches.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

Zhu

Luo

Deng

2020

Mathematical Problems in Engineering

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Cloud-based web applications are proliferating fast. Owing to the elastic capacity and diverse pricing schemes, cloud Infrastructure-as-a-Service (IaaS) offers great opportunity for web application providers to optimize resource cost. However, such optimization activities are confronting the challenges posed by the uncertainty of future demand and the increasing reservation contracts. This work investigates the problem of how to minimize IaaS rental cost associated with hosting web applications, while meeting the demand in the future business cycle. First, an integer liner program model is developed to optimize reservation-contract procurement, in which reserved and on-demand resources are planned for multiple provisioning stages as well as a long-term plan, e.g., twelve stages in an annual plan. Then, a Long Short-Term Memory (LSTM) based algorithm is designed to predict the workload in the future business cycle. In addition, the approaches for determining virtual instance capacity and the baseline workload of planning time slot are also presented. Finally, the experimental prediction results show the LSTM-based algorithm gains an advantage over several popular models, such as the Holter–Winters, the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Support Vector Regression (SVR). The simulations of resource planning show that the provisioning scheme based on our reservation-optimization model obtains significant cost savings than other typical provisioning schemes, while satisfying the demands.

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LSTM based reserve prediction for bank outlets

Cited by 21 publications

References 18 publications

Automatic modulation recognition based on CNN and GRU

Automatic modulation recognition based on CNN and GRU

Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

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