NDLI: Nonlinear Integration of Spatial and Temporal Forecasting by Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Jiaqiu Wang Tao Cheng Xia Li
Copyright Year	2007
Description	Author affiliation: Sun Yat-sen Univ., Guangzhou (Jiaqiu Wang)
Abstract	Spatio-temporal data mining is the extraction of unknown and implicit knowledge, structures, spatio-temporal relationships, or patterns not explicitly stored in spatio-temporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatio-temporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension respectively, or they are combined linearly as spatio-temporal integration. However, such linear combination of spatial and temporal dimensions is just a simplification of complicated spatio-temporal associations existing in complex geographical phenomena. In this study, The Support Vector Machines is introduced to construct nonlinear combination functions related to the spatial and temporal dimensions for integrated spatio-temporal forecasting. The proposed method has been tested by forecasting the annual average temperature of meteorological stations in P R China. The forecasting result shows that nonlinearly integrated spatio-temporal forecasting model via Support Vector Machines obtained better forecasting accuracy than those obtained by linear combination and other conventional methods.
Starting Page	61
Ending Page	66
File Size	271797
Page Count	6
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.424
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Temperature Statistical analysis Time series analysis Weather forecasting Predictive models Spatial databases Data mining Testing Meteorology
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Physical Analysis of Precipitation Factors Based on SVM Method

Analysis of solar generation and weather data in smart grid with simultaneous inference of nonlinear time series

A Novel Knowledge Discovery Model for Fishery Forecasting

Intelligent Prediction for Time Series Using Smooth Support Vector Regression

Multi-horizon ternary time series forecasting

Periodicity detection in time series databases

Nonlinear characteristics of daily temperature data and nonlinear modeling

Forecasting Time Series Water Levels on Mekong River Using Machine Learning Models

Spatio-Temporal data mining on MCS over Tibetan Plateau using satellite meteorological datasets

Nonlinear Integration of Spatial and Temporal Forecasting by Support Vector Machines

Similar Documents

Physical Analysis of Precipitation Factors Based on SVM Method

Analysis of solar generation and weather data in smart grid with simultaneous inference of nonlinear time series

A Novel Knowledge Discovery Model for Fishery Forecasting

Intelligent Prediction for Time Series Using Smooth Support Vector Regression

Multi-horizon ternary time series forecasting

Periodicity detection in time series databases

Nonlinear characteristics of daily temperature data and nonlinear modeling

Forecasting Time Series Water Levels on Mekong River Using Machine Learning Models

Spatio-Temporal data mining on MCS over Tibetan Plateau using satellite meteorological datasets

Nonlinear Integration of Spatial and Temporal Forecasting by Support Vector Machines