Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

Wang, Baojia; Liu, Pingzeng; Zhang, Chao; Wang, Junmei; Chen, Weijie; Cao, N.; O’Hare, Gregory M. P.; Wen, Fujiang

doi:10.32604/cmc.2018.03791

Cited by 47 publications

(38 citation statements)

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“…In the era of artificial intelligence based on big data analytics, traditional statistical methods and neural network approaches running on single machine may not be sufficient in processing large-scale datasets [25]. Therefore, in this article a nonlinear and parallel PSO-BP neural network approach is proposed and implemented on a distributed cluster to process the big data set of on-balance sheet item and off-balance sheet item for better prediction and efficient risk management.…”

Section: Related Workmentioning

confidence: 99%

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A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT

et al. 2019

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In recent years, the technology about IoT (Internet of Things) has been applied into finance domain, and the generated data, such as the real-time data of chattel mortgage supervision with GPS, sensors, network cameras, mobile devices, etc., has been used to improve the capability of financial credit risk management of bank loans. Financial credit risk is by far one of the most significant risks that commercial banks have to face, however, when confronting to the massively growing financial data from multiple sources including Internet, mobile networks or IoT, traditional statistical models and neural network models might not operate fairly or accurately enough for credit risk assessment with those diverse data. Hence, there is a practical need to establish more powerful risk prediction models with artificial intelligence based on big data analytics to predict default behaviors with better accuracy and capacity. In this article, a big data mining approach of Particle Swarm Optimization (PSO) based Backpropagation (BP) neural network is proposed for financial risk management in commercial banks with IoT deployment, which constructs a nonlinear parallel optimization model with Apache Spark and Hadoop HDFS techniques on the dataset of on-balance sheet item and off-balance sheet item. The experiment results indicate that this parallel risk management model has fast convergence rate and powerful predictive capacity, and performs efficiently in screening default behaviors. In the meanwhile, the distributed implementation on big data clusters largely reduces the processing time of model training and testing. INDEX TERMS Big data, artificial intelligence, financial risk management, Internet of Things, particle swarm optimization, BP neural network.

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Section: Related Workmentioning

confidence: 99%

“…In PSO based BP neural network model, the dimension of a particle in the population is denoted as in (25),…”

Section: Pso-bp Neural Network Modelmentioning

confidence: 99%

A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT

et al. 2019

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“…Wang et al [21] and Zhou et al [22] studied the change rule of video popularity over time, and applied the results to design video caching strategies for CDNs. Dodds and Watts [23] regarded each item as a contagious disease that disseminates through the social connections, and built an infective model to spread the information. On the contrary, we design a novel propagation model by exploiting the association between video contents and social relationships among users for estimating future demands.…”

Section: B Social Media Propagationmentioning

confidence: 99%

“…There are two cases for a user to see a video v: 1) the application platform analyzes the videos that the user has watched and recommends a close video v to him, and 2) the friends of this user who have viewed the video v and appraised it in the instant messaging software (i.e., microblog). We build the video propagation model upon the social community model [23], and explore the social connections among users to estimate the potential viewing requests of an individual video.…”

Section: Basic Scheme a Estimating Potential Demandsmentioning

confidence: 99%

Cost-Optimized Video Dissemination Over Heterogeneous Cloud With SLAs Support

Ruan

2019

IEEE Access

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A growing number of social video distribution services are turning toward to the cloud-based architecture for lower cost and better scalability. Although appealing, such a cloud content delivery network (cloud-based CDN) involves two key tasks: to dynamically migrate the contents across multiple data centers on diverse locations and to place user requests in the proper sites such that the monetary cost and service latency are targeted appropriately. In this paper, we formulate these two objectives into a combinational optimization problem and develop a context-aware community-based computing model to discover the contextual information of video propagation. In particular, we explore the social graph that people created to estimate the potential demands of each video and to propose a basic scheme for video migration and request placement under the heterogeneous cloud paradigm. Since the basic scheme adopts a greedy algorithm per step, it only obtains the short-term optimality. To solve this problem, we design an advanced scheme that can align to the long-term optimum solution and can ensure that the total monetary cost is minimized while meeting the predefined service level agreements (SLAs). Compared with present scheduling techniques, we found that the designed algorithm achieves lower service cost yet guarantees that the response time of requests is all within the specified quality of service (QoS) requirements. INDEX TERMS Video sharing, context-aware services, estimation theory.

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“…In most of the previous studies, they mainly conducted short-term predictions of haze pollutant concentrations. Besides, some prediction models are a little complex and converge slowly, failing to mine potential information from data [Wang, Liu, Zhang et al (2018)]. Therefore, motivated from these solutions, we propose an algorithm named Monte Carlo to predict the monthly average value of haze concentration.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

Ding¹,

Wang²,

Liu³

2019

Journal on Big Data

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Haze concentration prediction, especially PM2.5, has always been a significant focus of air quality research, which is necessary to start a deep study. Aimed at predicting the monthly average concentration of PM2.5 in Beijing, a novel method based on Monte Carlo model is conducted. In order to fully exploit the value of PM2.5 data, we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively. The results show that these data are both approximately normal distribution. On the basis of the results, a Monte Carlo method can be applied to establish a probability model of normal distribution based on two different variables and random sampling numbers can also be generated by computer. Through a large number of simulation experiments, the average monthly concentration of PM2.5 in Beijing and the general trend of PM2.5 can be obtained. By comparing the errors between the real data and the predicted data, the Monte Carlo method is reliable in predicting the PM2.5 monthly mean concentration in the area. This study also provides a feasible method that may be applied in other studies to predict other pollutants with large scale time series data.

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Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

Cited by 47 publications

References 0 publications

A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT

A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT

Cost-Optimized Video Dissemination Over Heterogeneous Cloud With SLAs Support

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

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