Multi-Fractality Analysis of Time Series in Artificial Stock Market Generated by Multi-Agent Systems Based on the Genetic Programming and Its Applications

2007 ◽  
Vol E90-A (10) ◽  
pp. 2212-2222 ◽  
Author(s):  
Y. IKEDA ◽  
S. TOKINAGA
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Genetic Programming ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Artificial Stock Market ◽  
Analysis Of Time Series

EARTHQUAKE TIME PREDICTION WITH MULTI-AGENT SYSTEMS

2020 ◽  
Vol 69 (1) ◽  
pp. 275-279
Author(s):  
A.A. Abyurova ◽  
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Research Area ◽  
Seismic Signals ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Time Prediction ◽  
Python Programming Language ◽  
Multi Agent ◽  
Python Programming

This article is devoted to the development of multi-agent systems for predicting the time of earthquakes based on seismic signals. The work uses a dataset from laboratory signals, which used to calculate the time predicted before the next earthquake. The MadKIT platform and the Python programming language are used to build multi-agent systems. The "tsfresh" package is used to calculate a large number of time series characteristics, so-called features, from seismic signals for further use in regression. The article considers one of the regression models - LightGBM. Using it, a set of data was processed and the predicted time of the earthquake was obtained. This article shows the relevance and prospects of the research area, describes the functionality of the created agents

scholarly journals Editorial For Vol.28, No.1

2020 ◽  
Vol 28 (1) ◽  
pp. i-ii
Keyword(s):  
Time Series ◽  
Software Engineering ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

The current, March 2020 (Vol. 28, No. 1), issue of CIT brings papers from the areas of multi-agent systems, time series, software engineering and cybercrime detection, whose respective short summaries traditionally follow below.

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