scholarly journals Short-time prediction of chaotic laser using time-delayed photonic reservoir computing

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Reservoir Computing ◽  
Time Prediction ◽  
Chaotic Laser ◽  
Short Time

scholarly journals Earthquake forecasting and its verification

2005 ◽  
Vol 12 (6) ◽  
pp. 965-977 ◽  
Author(s):  
J. R. Holliday ◽  
K. Z. Nanjo ◽  
K. F. Tiampo ◽  
J. B. Rundle ◽  
D. L. Turcotte
Keyword(s):  
Temporal Variations ◽  
Earthquake Forecasting ◽  
Earthquake Risk ◽  
Decision Threshold ◽  
New Approach ◽  
Pi Method ◽  
Time Prediction ◽  
Short Time ◽  
Large Earthquakes ◽  
Proven Method

Abstract. No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ("hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver) operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis

2013 ◽  
Vol 712-715 ◽  
pp. 1550-1554
Author(s):  
Xin Dong Yang ◽  
Zuo Chao Wang ◽  
Ai Guo Shi ◽  
Bo Liu ◽  
Li Li
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Chaotic Time Series ◽  
Ship Motion ◽  
Ship Motions ◽  
Sea State ◽  
Time Prediction ◽  
Series Analysis ◽  
Short Time ◽  
Chaotic Time Series Analysis

Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

scholarly journals Urban air pollution by odor sources: Short time prediction

2015 ◽  
Vol 122 ◽  
pp. 74-82 ◽  
Author(s):  
Nicola Pettarin ◽  
Marina Campolo ◽  
Alfredo Soldati
Keyword(s):  
Air Pollution ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Time Prediction ◽  
Short Time

scholarly journals Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

Buildings ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 198
Author(s):  
Attoue ◽  
Shahrour ◽  
Mroueh ◽  
Younes
Keyword(s):  
Thermal Behavior ◽  
Thermal Conditions ◽  
Experimental Tests ◽  
Heating Power ◽  
Experimental Results ◽  
Optimal Order ◽  
Time Prediction ◽  
Box Models ◽  
Short Time

The use of grey-box models for short-time forecasting of buildings’ thermal behavior requires the determination of the models’ order since this order could influence the grey-box models’ performance. This paper presents an analysis of the optimal order of these models for different thermal conditions. The novelty of this work consists of considering the influence of the heating conditions on the determination of the performances of grey-box models. The analysis is based on experimental tests that were conducted in a room with different thermal conditions, related to the variation of the heating power. Experimental results were used for the determination of the optimal grey-box models’ order that minimizes the gap between the experimental results and the grey-box forecasting. Results show that the optimal grey-box models’ order depends on the buildings’ thermal conditions, but generally lies between two and three with an error less than 0.2 °C and a fit percent greater than 90%.

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