scholarly journals Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaoliang Chen ◽  
Xiang Lan ◽  
Jihong Wan ◽  
Peng Lu ◽  
Ming Yang
Keyword(s):  
Time Series ◽  
Social Network ◽  
Prediction Model ◽  
Linear Models ◽  
Personalized Recommendation ◽  
Popularity Dynamics ◽  
Social Media Platforms ◽  
Popularity Prediction ◽  
Prediction Approach ◽  
Prediction Problems

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

Network Security Situation Prediction Using Artificial Immune System and Phase Space Reconstruction

2010 ◽  
Vol 44-47 ◽  
pp. 3662-3666 ◽  
Author(s):  
Yuan Quan Shi ◽  
Tao Li ◽  
Wen Chen ◽  
Yan Ming Fu
Keyword(s):  
Time Series ◽  
Immune System ◽  
Phase Space ◽  
Network Security ◽  
Prediction Model ◽  
Artificial Immune System ◽  
Proper Time ◽  
Phase Space Reconstruction ◽  
Artificial Immune ◽  
Prediction Approach

To solve network security situation prediction problem, a novel Prediction approach for network security situation based on artificial Immune system and Phase Space Reconstruction (PIPSR) is proposed. In PIPSR, we use phase space reconstruction to analyze the time series of network security situation and to reconstruct proper time series phase space; then, we use immune evolution mechanism to construct corresponding prediction model for network security situation; and lastly we use this prediction model to forecast network security situation. The simulation results show that PIPSR can more exactly forecast the future network security situation than GAP and BPNNP.

Analysis and prediction of groundwater level time series with Autoregressive Linear Models

2016 ◽  
Vol 39 ◽  
pp. 109-112
Author(s):  
Mirko Ginocchi ◽  
Giovanni Franco Crosta ◽  
Marco Rotiroti ◽  
Tullia Bonomi
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Linear Models

GROUNDWATER TIME SERIES

1970 ◽  
Vol 1 (3) ◽  
pp. 181-205 ◽  
Author(s):  
ERIK ERIKSSON
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Hydrologic Models ◽  
Physical Point ◽  
Run Off ◽  
Fort Collins ◽  
Statistical Sense ◽  
Full Benefit ◽  
Future Work ◽  
Prediction Problems

The term “stochastic hydrology” implies a statistical approach to hydrologic problems as opposed to classic hydrology which can be considered deterministic in its approach. During the International Hydrology Symposium, held 6-8 September 1967 at Fort Collins, a number of hydrology papers were presented consisting to a large extent of studies on long records of hydrological elements such as river run-off, these being treated as time series in the statistical sense. This approach is, no doubt, of importance for future work especially in relation to prediction problems, and there seems to be no fundamental difficulty for introducing the stochastic concepts into various hydrologic models. There is, however, some developmental work required – not to speak of educational in respect to hydrologists – before the full benefit of the technique is obtained. The present paper is to some extent an exercise in the statistical study of hydrological time series – far from complete – and to some extent an effort to interpret certain features of such time series from a physical point of view. The material used is 30 years of groundwater level observations in an esker south of Uppsala, the observations being discussed recently by Hallgren & Sands-borg (1968).

scholarly journals Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts With Time Series

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17644-17653 ◽  
Author(s):  
Lei Li ◽  
Yabin Wu ◽  
Yuwei Zhang ◽  
Tianyuan Zhao
Keyword(s):  
Time Series ◽  
Social Network

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices

2018 ◽  
Vol 1 (1-2) ◽  
pp. 29-38 ◽  
Author(s):  
Imran Hossain Newton ◽  
A. F. M Tariqul Islam ◽  
A. K. M. Saiful Islam ◽  
G. M. Tarekul Islam ◽  
Anika Tahsin ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Vegetation Indices ◽  
Yield Prediction ◽  
Time Series Images
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