Enterprise Financial Management and Trend Prediction Based on Time Series Analytics and Edge Computing

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

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Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Wireless Communications and Mobile Computing ◽

10.1155/2021/5566647 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ying Zhu

Keyword(s):

Time Series ◽

Public Opinion ◽

Chaotic Time Series ◽

Edge Computing ◽

Factor Graph ◽

New Paradigm ◽

Placement Problem ◽

Index Method ◽

Service Placement ◽

Network Public Opinion

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

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A Time Series Prediction Approach Based on Hybrid Tuning for Database Performance Indicator in AIOps

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203851030 ◽

2020 ◽

Vol 38 (5) ◽

pp. 1030-1037

Author(s):

Xiaoling Wang ◽

Xiaofang Zhang ◽

Ning Li ◽

Chengfeng Han ◽

Zhuping Yuan ◽

...

Keyword(s):

Time Series ◽

Model Building ◽

Performance Indicator ◽

Time Series Prediction ◽

Arima Model ◽

Parameter Tuning ◽

Trend Prediction ◽

Cloud Database ◽

Variation Data ◽

Prediction Approach

One of the most important applications of the intelligent operation and maintenance of a cloud database is its trend prediction of key performance indicators (KPI), such as disk use, memory use, etc. We propose a method named AutoPA4DB (Auto Prophet and ARIMA for Database) to predict the trend of the KPIs of the cloud database based on the Prophet model and the ARIMA model. Our AutoPA4DB method includes data preprocessing, model building, parameter tuning and optimization. We employ the weighted MAPE coverage to measure its accuracy and use 6 industrial datasets including 10 KPIs to compare the AutoPA4DB method with other three time-series trend prediction algorithms. The experimental results show that our AutoPA4DB method performs best in predicting monotonic variation data, e.g.disk use trend prediction. But it is unstable in predicting oscillatory variation data; for example, it is acceptable in memory use trend prediction but has poor accuracy in predicting the number of database connection trends.

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