scholarly journals On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics

2019 ◽  
pp. 352-355
Author(s):  
Julien Martinelli ◽  
Jeremy Grignard ◽  
Sylvain Soliman ◽  
François Fages
Keyword(s):  
Time Series ◽  
Statistical Learning ◽  
Greedy Heuristics

Study on Prediction of Mackey-Glass Chaotic Time Series Using Support Vector Machines

2014 ◽  
Vol 1051 ◽  
pp. 1009-1015 ◽  
Author(s):  
Ya Li Ning ◽  
Xin You Wang ◽  
Xi Ping He
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Support Vector Machines ◽  
Statistical Learning ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Machine Type ◽  
Type Selection ◽  
Vector Machines ◽  
New Generation

Support Vector Machines (SVM), which is a new generation learning method based on advances in statistical learning theory, is characterized by the use of many standard technologies of machine learning such as maximal margin hyperplane, Mercel kernels and the quadratic programming. Because the best performance is obtained in many currently challenging applications, SVM has sustained wide attention, and has been become the standard tools of machine learning and data mining. But as a developing technology, SVM still have some problems and its applications are limited. In this paper, SVM and its applications in chaotic time series including predicting chaotic time series, focus on comparison in regression type selection, and kernel type selection in the same regression machine type.

Algorithms Research in the Application of Lorenz Time Series Prediction

2011 ◽  
Vol 268-270 ◽  
pp. 1017-1020
Author(s):  
Man Xiang Miao ◽  
Yi Jin Gang
Keyword(s):  
Time Series ◽  
Statistical Learning ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Rbf Networks ◽  
Machine Learning Methods ◽  
Simulation Results ◽  
Time Series Simulation ◽  
Better Than

Prediction of Lorenz Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine (SVM) is a kind of novel machine learning methods based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper combined SVM with neural network which based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Lorenz Chaotic Time Series. Simulation results show that the effect is better than other methods.

scholarly journals Prediction of time series by statistical learning: general losses and fast rates

2013 ◽  
Vol 1 ◽  
Author(s):  
Pierre Alquier ◽  
Xiaoyin Li ◽  
Olivier Wintenberger
Keyword(s):  
Time Series ◽  
Statistical Learning ◽  
Fast Rates

scholarly journals Statistical Learning With Time Series Dependence: An Application to Scoring Sleep in Mice

2013 ◽  
Vol 108 (504) ◽  
pp. 1147-1162 ◽  
Author(s):  
Blakeley B. McShane ◽  
Shane T. Jensen ◽  
Allan I. Pack ◽  
Abraham J. Wyner
Keyword(s):  
Time Series ◽  
Statistical Learning ◽  
Series Dependence

scholarly journals Detection of hydrogeochemical seismic precursors by a statistical learning model

2008 ◽  
Vol 8 (6) ◽  
pp. 1207-1216 ◽  
Author(s):  
L. Castellana ◽  
P. F. Biagi
Keyword(s):  
Time Series ◽  
Statistical Learning ◽  
Nearest Neighbor ◽  
Earthquake Precursor ◽  
Error Rates ◽  
Classification Error ◽  
K Nearest Neighbor ◽  
Seismic Precursors ◽  
Seismic Precursor ◽  
Natural Spring

Abstract. The problem of detecting the occurrence of an earthquake precursor is faced in the general framework of the statistical learning theory. The aim of this work is both to build models able to detect seismic precursors from time series of different geochemical signals and to provide an estimate of number of false positives. The model we used is k-Nearest-Neighbor classifier for discriminating "no-disturbed signal", "seismic precursor" and "co-post seismic precursor" in time series relative to thirteen different hydrogeochemical parameters collected in water samples from a natural spring in Kamchachta (Russia) peninsula. The measurements collected are ion content (Na, Cl, Ca, HCO3, H3BO3), parameters (pH, Q, T) and gases (N2, CO2, CH4, O2, Ag). The classification error is measured by Leave-K-Out-Cross-Validation procedure. Our study shows that the most discriminative ions for detecting seismic precursors are Cl and Na having an error rates of 15%. Moreover, the most discriminative parameters and gases are Q and CH4 respectively, with error rate of 21%. The ions result the most informative hydrogeochemicals for detecting seismic precursors due to the peculiarities of the mechanisms involved in earthquake preparation. Finally we show that the information collected some month before the event under analysis are necessary to improve the classification accuracy.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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