Discussion and Analysis on the Application of Passive Network Model Based on APRIORI Algorithm and Time Series Dimensions

2020 ◽  
Author(s):  
Zipei Tan ◽  
Zinan Yang ◽  
Qu Luo
Keyword(s):  
Time Series ◽  
Network Model ◽  
Apriori Algorithm ◽  
Model Based ◽  
Passive Network

scholarly journals Wavelet Network Model Based on Multiple Criteria Decision Making for Forecasting Temperature Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-4 ◽  
Author(s):  
Jian Zhang ◽  
Xiao-hua Yang ◽  
Xiao-juan Chen
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Relative Error ◽  
Network Model ◽  
Multiple Criteria Decision Making ◽  
Temperature Time Series ◽  
Multiple Criteria ◽  
Wavelet Network ◽  
Model Based ◽  
Temperature Time

Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regression (NNBR), the probability of relative error smaller than 10% increases from 65.79% to 84.21% (forecast periodT=1) and from 51.35% to 91.89%(T=2)by WNMCDM model. Similarly, the probability of relative error smaller than 20% increases from 84.21% to 97.37%(T=1)and from 81.08% to 91.89%(T=2)by WNMCDM model. Therefore, WNMCDM model is superior to NNBR model in forecasting temperature time series.

scholarly journals SOMA Network Model Based on Native Visibility Graph

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 49-56
Author(s):  
Lukas Tomaszek ◽  
Ivan Zelinka
Keyword(s):  
Time Series ◽  
Network Model ◽  
Visibility Graph ◽  
New Model ◽  
Model Based ◽  
Network Properties ◽  
Basic Network ◽  
Self Organizing

In this article, we want to propose a new model of the network for analyzing the evolution algorithms.We focus on the graph called native visibility graph. We show how we can get a time series from the run ofthe self-organizing migrating algorithm and how we can convert these series into a network. At the end of thearticle, we focus on some basic network properties and we propose how can we use these properties for laterinvestigation. All experiments run on well-known CEC 2016 benchmarks.

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