Multigranular Wavelet Decomposition-Based Support Vector Regression and Moving Average Method for Service-Time Prediction on Web Map Service Platforms

Dong, Guangsheng; Li, Rui; Jiang, Jane; Wu, Huayi; McClure, Stephen C.

doi:10.1109/jsyst.2019.2944527

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…Kim S T [12] combines granularity with neural networks and is applied to efficient knowledge discovery. Dong G [13] elaborates on the connection between concept description and concept hierarchy transformation based on the similarity of the concept lattice and granularity partitioning in the process of concept clustering. Su W H [14] grain vector space and artificial neural network, which improves the timeliness and comprehensibility of knowledge representation of the artificial neural network.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

Yang

2022

Journal of Robotics

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With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. At the same time, the granulation idea of grain computing is introduced into time-series analysis, and the original high-dimensional time series is granulated into low-dimensional grain time series by information granulation of time series, and the constructed information grains can portray and reflect the structural characteristics of the original time-series data, to realize efficient dimensionality reduction and lay the foundation for the subsequent data mining work. Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model.

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Section: Related Workmentioning

confidence: 99%

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

Yang

2022

Journal of Robotics

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“…POI data provides spatial details and the attributes of buildings, which are important for the quantitative identification of building types in cities. According to a previous study [35], over 20 types of POI data are available, and they can be divided into three categories comprising commercial service, public service, and residential building (Table 1). Considering the geometric distribution of POI and buildings, the relationships between them can be divided into three categories: (1) buildings with only a single type of POI, such as public service POIs; (2) various types of POIs related to buildings, such as residential POIs and commercial service POIs; and (3) buildings with no POI types.…”

Section: Geometric Relationships and Classification Of Spacementioning

confidence: 99%

Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan

Wang

Jiang

et al. 2021

Future Internet

Self Cite

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In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.

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GeoNLU: Bridging the gap between natural language and spatial data infrastructures

Naveen,

Maheswar,

Trojovský

2024

Alexandria Engineering Journal

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Multigranular Wavelet Decomposition-Based Support Vector Regression and Moving Average Method for Service-Time Prediction on Web Map Service Platforms

Cited by 11 publications

References 37 publications

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan

GeoNLU: Bridging the gap between natural language and spatial data infrastructures

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