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19 results on '"Lungaroni, Michele"'

3. A Metric to Improve the Robustness of Conformal Predictors in the Presence of Error Bars

Author

Murari, Andrea, Talebzadeh, Saeed, Vega, Jesús, Peluso, Emmanuele, Gelfusa, Michela, Lungaroni, Michele, Gaudio, Pasqualino, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Gammerman, Alexander, editor, Luo, Zhiyuan, editor, Vega, Jesús, editor, and Vovk, Vladimir, editor

Published
2016
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12. Adaptive Learning for Disruption Prediction

Author

GELFUSA Michela, MURARI Andrea, LUNGARONI Michele, PELUSO Emmanuele, GAUDIO Pasqualino, VEGA Jesus, and DORMIDO-CANTO Sebastian

Subjects
Tokamak, Disruption Prediction
Abstract

Accurate prediction of catastrophic events is becoming an important area of investigation in many research fields. In Tokamaks, detecting disruptions with sufficient anticipation time is a prerequisite to undertaking any remedial strategy, either for mitigation or for avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but tend to age very quickly. Such a weakness is a consequence of the i.i.d. (independent an identically distributed) assumption on which they are based, which means that the input data are independent and are sampled from exactly the same probability distribution for the training set, the test set and the final actual discharges. These hypotheses are certainly not verified in practice, since nowadays the experimental programmes of fusion devices evolve quite rapidly and metallic machines are very sensitive to small changes in the plasma conditions. This paper describes various adaptive training strategies that have been develop to preserve the performance of disruption predictors in non-stationary conditions. The proposed techniques are based new ensembles of classifiers, belonging to the CART (Classification and Regression Trees) family. The improvements in performance are remarkable and the final predictors satisfy the requirements of the next generation of experimental devices.

Published
2018

13. Causality detection methods for time series analysis

Author

CRACIUNESCU Teddy, MURARI Andrea, PELUSO Emmanuele, GELFUSA Michela, LUNGARONI Michele, and GAUDIO Pasquale

Subjects
time series analysis, causality detection methods
Abstract

Coupling and synchronization are common phenomena that occur in nature, e.g. in biological, physiological and environmental systems, as well as in physics and engineered systems. Depending on the coupling strength, the systems may undergo phase synchronization, generalized synchronization, lag synchronization and complete synchronization [1]. Lag or intermittent lag synchronization, where the difference between the output of one system and the time-delayed output of a second system are asymptotically bounded, is the typical case of the fusion plasma instability control by pace-making techniques [2-3]. The major issue, in determining the efficiency of the pacing techniques, resides in the periodic or quasiperiodic nature of the plasma instabilities occurrence. After the perturbation induced by the control systems, if enough time is allowed to pass, the instabilities are bound to reoccur. Therefore, it is crucial to determine an appropriate interval over which the pacing techniques can have a real influence and an effective triggering capability. Several independent classes of statistical indicators developed to address this issue are presented. The transfer entropy [4] is a powerful tool for measuring the causation between dynamical events. The amount of information exchanged between two systems depends not only the magnitude but also the direction of the cause-effect relation. Recurrence plots (RP) is an advanced technique of nonlinear data analysis, revealing all the times when the phase space trajectory of the dynamical system visits roughly the same area in the phase space [5]. RP refinement, called joint recurrence plots (JRPs), can be used to relate the behavior of one signal with the one of another. Convergent cross mapping (CCM) [6], tests for causation by measuring the extent to which the historical record of one time series Y values can reliably estimate states of another time series X. CCM searches for the signature of X in Y's time series by detecting whether there is a correspondence between the "library" of points, in the attractor manifold built from Y, and points in the X manifold. The two manifolds are constructed from lagged coordinates of the two time-series. The Weighted Cross Visibility Graph (WCVG) method [7] starts from the idea of mapping the coupled time series into a complex network [8] and evaluates its structural complexity by mean of the Shannon entropy of the modified adjacency matrix (constructed by weighting the connections with the metric distance between two connected values in the time series).

Published
2018

14. Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools.

Author

Murari, Andrea, Peluso, Emmanuele, Lungaroni, Michele, Rossi, Riccardo, and Gelfusa, Michela

Subjects
MACHINE learning, NAIVE Bayes classification, MACHINE tools, GENETIC programming, SUPPORT vector machines, PHYSICS
Abstract

Featured Application: Machine-learning-based techniques have been applied to disruption prediction in Tokamaks and, by symbolic regression via genetic programming, physically meaningful equations have been extracted from the machine learning models, helping to investigate disruption physics and to transfer present-day knowledge to future devices. The inadequacies of basic physics models for disruption prediction have induced the community to increasingly rely on data mining tools. In the last decade, it has been shown how machine learning predictors can achieve a much better performance than those obtained with manually identified thresholds or empirical descriptions of the plasma stability limits. The main criticisms of these techniques focus therefore on two different but interrelated issues: poor "physics fidelity" and limited interpretability. Insufficient "physics fidelity" refers to the fact that the mathematical models of most data mining tools do not reflect the physics of the underlying phenomena. Moreover, they implement a black box approach to learning, which results in very poor interpretability of their outputs. To overcome or at least mitigate these limitations, a general methodology has been devised and tested, with the objective of combining the predictive capability of machine learning tools with the expression of the operational boundary in terms of traditional equations more suited to understanding the underlying physics. The proposed approach relies on the application of machine learning classifiers (such as Support Vector Machines or Classification Trees) and Symbolic Regression via Genetic Programming directly to experimental databases. The results are very encouraging. The obtained equations of the boundary between the safe and disruptive regions of the operational space present almost the same performance as the machine learning classifiers, based on completely independent learning techniques. Moreover, these models possess significantly better predictive power than traditional representations, such as the Hugill or the beta limit. More importantly, they are realistic and intuitive mathematical formulas, which are well suited to supporting theoretical understanding and to benchmarking empirical models. They can also be deployed easily and efficiently in real-time feedback systems. [ABSTRACT FROM AUTHOR]

Published
2020
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17. A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

Author

Gelfusa, Michela, primary, Murari, Andrea, additional, Lungaroni, Michele, additional, Malizia, Andrea, additional, Parracino, Stefano, additional, Peluso, Emmanuele, additional, Cenciarelli, Orlando, additional, Carestia, Mariachiara, additional, Pizzoferrato, Roberto, additional, Vega, Jesus, additional, and Gaudio, Pasquale, additional

Published
2016
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18. On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

Author

Murari, Andrea, Lungaroni, Michele, Peluso, Emmanuele, Gaudio, Pasquale, Lerche, Ernesto, Garzotti, Luca, Gelfusa, Michela, Abduallev, S., Abhangi, M., Abreu, P., Afzal, M., Aggarwal, K. M., Ahlgren, T., Ahn, J. H., Aho-Mantila, L., Aiba, N., Airila, M., Albanese, R., Aldred, V., Alegre, D., Alessi, E., Aleynikov, P., Alfier, A., Alkseev, A., Allinson, M., Alper, B., Alves, E., Ambrosino, G., Ambrosino, R., Amicucci, L., Amosov, V., Sunden, E. Andersson, Angelone, M., Anghel, M., Angioni, C., Appel, L., Appelbee, C., Arena, P., Ariola, M., Arnichand, H., Arshad, S., Ash, A., Ashikawa, N., Aslanyan, V., Asunta, O., Auriemma, F., Austin, Y., Avotina, L., Axton, M. D., Ayres, C., Bacharis, M., Baciero, A., Baiao, D., Bailey, S., Baker, A., Balboa, I., Balden, M., Balshaw, N., Bament, R., Banks, J. W., Baranov, Y. F., Barnard, M. A., Barnes, D., Barnes, M., Barnsley, R., Wiechec, A. Baron, Orte, L. Barrera, Baruzzo, M., Basiuk, V., Bassan, M., Bastow, R., Batista, A., Batistoni, P., Baughan, R., Bauvir, B., Baylor, L., Bazylev, B., Beal, J., Beaumont, P. S., Beckers, M., Beckett, B., Becoulet, A., Bekris, N., Beldishevski, M., Bell, K., Belli, F., Bellinger, M., Belonohy, E., Ben Ayed, N., Benterman, N. A., Bergsaker, H., Bernardo, J., Bernert, M., Berry, M., Bertalot, L., Besliu, C., Beurskens, M., Bieg, B., Bielecki, J., Biewer, T., Bigi, M., Bilkova, P., Binda, F., Bisoffi, A., Bizarro, J. P. S., Bjorkas, C., Blackburn, J., Blackman, K., Blackman, T. R., Blanchard, P., Blatchford, P., Bobkov, V., Boboc, A., Bodnar, G., Bogar, O., Bolshakova, I., Bolzonella, T., Bonanomi, N., Bonelli, F., Boom, J., Booth, J., Borba, D., Borodin, D., Borodkina, I., Botrugno, A., Bottereau, C., Boulting, P., Bourdelle, C., Bowden, M., Bower, C., Bowman, C., Boyce, T., Boyd, C., Boyer, H. J., Bradshaw, J. M. A., Braic, V., Bravanec, R., Breizman, B., Bremond, S., Brennan, P. D., Breton, S., Brett, A., Brezinsek, S., Bright, M. D. J., Brix, M., Broeckx, W., Brombin, M., Broslawski, A., Brown, D. P. D., Brown, M., Bruno, E., Bucalossi, J., Buch, J., Buchanan, J., Buckley, M. A., Budny, R., Bufferand, H., Bulman, M., Bulmer, N., Bunting, P., Buratti, P., Burckhart, A., Buscarino, A., Busse, A., Butler, N. K., Bykov, I., Byrne, J., Cahyna, P., Calabro, G., Calvo, I., Camenen, Y., Camp, P., Campling, D. C., Cane, J., Cannas, B., Capel, A. J., Card, P. J., Cardinali, A., Carman, P., Carr, M., Carralero, D., Carraro, L., Carvalho, B. B., Carvalho, I., Carvalho, P., Casson, F. J., Castaldo, C., Catarino, N., Caumont, J., Causa, F., Cavazzana, R., Cave-Ayland, K., Cavinato, M., Cecconello, M., Ceccuzzi, S., Cecil, E., Cenedese, A., Cesario, R., Challis, C. D., Chandler, M., Chandra, D., Chang, C. S., Chankin, A., Chapman, I. T., Chapman, S. C., Chernyshova, M., Chitarin, G., Ciraolo, G., Ciric, D., Citrin, J., Clairet, F., Clark, E., Clark, M., Clarkson, R., Clatworthy, D., Clements, C., Cleverly, M., Coad, J. P., Coates, P. A., Cobalt, A., Coccorese, V., Cocilovo, V., Coda, S., Coelho, R., Coenen, J. W., Coffey, I., Colas, L., Collins, S., Conka, D., Conroy, S., Conway, N., Coombs, D., Cooper, D., Cooper, S. R., Corradino, C., Corre, Y., Corrigan, G., Cortes, S., Coster, D., Couchman, A. S., Cox, M. P., Craciunescu, T., Cramp, S., Craven, R., Crisanti, F., Croci, G., Croft, D., Crombe, K., Crowe, R., Cruz, N., Cseh, G., Cufar, A., Cullen, A., Curuia, M., Czarnecka, A., Dabirikhah, H., Dalgliesh, P., Dalley, S., Dankowski, J., Darrow, D., Davies, O., Davis, W., Day, C., Day, I. E., De Bock, M., de Castro, A., de la Cal, E., de la Luna, E., De Masi, G., de Pablos, J. L., De Temmerman, G., De Tommasi, G., de Vries, P., Deakin, K., Deane, J., Agostini, F. Degli, Dejarnac, R., Delabie, E., den Harder, N., Dendy, R. O., Denis, J., Denner, P., Devaux, S., Devynck, P., Di Maio, F., Di Siena, A., Di Troia, C., Dinca, P., D'Inca, R., Ding, B., Dittmar, T., Doerk, H., Doerner, R. P., Donne, T., Dorling, S. E., Dormido-Canto, S., Doswon, S., Douai, D., Doyle, P. T., Drenik, A., Drewelow, P., Drews, P., Duckworth, Ph., Dumont, R., Dumortier, P., Dunai, D., Dunne, M., Duran, I., Durodie, F., Dutta, P., Duval, B. P., Dux, R., Dylst, K., Dzysiuk, N., Edappala, P. V., Edmond, J., Edwards, A. M., Edwards, J., Eich, Th., Ekedahl, A., El-Jorf, R., Elsmore, C. G., Enachescu, M., Ericsson, G., Eriksson, F., Eriksson, J., Eriksson, L. G., Esposito, B., Esquembri, S., Esser, H. G., Esteve, D., Evans, B., Evans, G. E., Evison, G., Ewart, G. D., Fagan, D., Faitsch, M., Falie, D., Fanni, A., Fasoli, A., Faustin, J. M., Fawlk, N., Fazendeiro, L., Fedorczak, N., Felton, R. C., Fenton, K., Fernades, A., Fernandes, H., Ferreira, J., Fessey, J. A., Fevrier, O., Ficker, O., Field, A., Fietz, S., Figueiredo, A., Figueiredo, J., Fil, A., Finburg, P., Firdaouss, M., Fischer, U., Fittill, L., Fitzgerald, M., Flammini, D., Flanagan, J., Fleming, C., Flinders, K., Fonnesu, N., Fontdecaba, J. M., Formisano, A., Forsythe, L., Fortuna, L., Fortuna-Zalesna, E., Fortune, M., Foster, S., Franke, T., Franklin, T., Frasca, M., Frassinetti, L., Freisinger, M., Fresa, R., Frigione, D., Fuchs, V., Fuller, D., Futatani, S., Fyvie, J., Gal, K., Galassi, D., Galazka, K., Galdon-Quiroga, J., Gallagher, J., Gallart, D., Galvao, R., Gao, X., Gao, Y., Garcia, J., Garcia-Carrasco, A., Garcia-Munoz, M., Gardarein, J. -L., Garzotti, L., Gaudio, P., Gauthier, E., Gear, D. F., Gee, S. J., Geiger, B., Gelfusa, M., Gerasimov, S., Gervasini, G., Gethins, M., Ghani, Z., Ghate, M., Gherendi, M., Giacalone, J. C., Giacomelli, L., Gibson, C. S., Giegerich, T., Gil, C., Gil, L., Gilligan, S., Gin, D., Giovannozzi, E., Girardo, J. B., Giroud, C., Giruzzi, G., Gloeggler, S., Godwin, J., Goff, J., Gohil, P., Goloborod'ko, V., Gomes, R., Goncalves, B., Goniche, M., Goodliffe, M., Goodyear, A., Gorini, G., Gosk, M., Goulding, R., Goussarov, A., Gowland, R., Graham, B., Graham, M. E., Graves, J. P., Grazier, N., Grazier, P., Green, N. R., Greuner, H., Grierson, B., Griph, F. S., Grisolia, C., Grist, D., Groth, M., Grove, R., Grundy, C. N., Grzonka, J., Guard, D., Guerard, C., Guillemaut, C., Guirlet, R., Gurl, C., Utoh, H. H., Hackett, L. J., Hacquin, S., Hagar, A., Hager, R., Hakola, A., Halitovs, M., Hall, S. J., Cook, S. P. Hallworth, Hamlyn-Harris, C., Hammond, K., Harrington, C., Harrison, J., Harting, D., Hasenbeck, F., Hatano, Y., Hatch, D. R., Haupt, T. D. V., Hawes, J., Hawkes, N. C., Hawkins, J., Hawkins, P., Haydon, P. W., Hayter, N., Hazel, S., Heesterman, P. J. L., Heinola, K., Hellesen, C., Hellsten, T., Helou, W., Hemming, O. N., Hender, T. C., Henderson, M., Henderson, S. S., Henriques, R., Hepple, D., Hermon, G., Hertout, P., Hidalgo, C., Highcock, E. G., Hill, M., Hillairet, J., Hillesheim, J., Hillis, D., Hizanidis, K., Hjalmarsson, A., Hobirk, J., Hodille, E., Hogben, C. H. A., Hogeweij, G. M. D., Hollingsworth, A., Hollis, S., Homfray, D. A., Horacek, J., Hornung, G., Horton, A. R., Horton, L. D., Horvath, L., Hotchin, S. P., Hough, M. R., Howarth, P. J., Hubbard, A., Huber, A., Huber, V., Huddleston, T. M., Hughes, M., Huijsmans, G. T. A., Hunter, C. L., Huynh, P., Hynes, A. M., Iglesias, D., Imazawa, N., Imbeaux, F., Imrisek, M., Incelli, M., Innocente, P., Irishkin, M., Ivanova-Stanik, I., Jachmich, S., Jacobsen, A. S., Jacquet, P., Jansons, J., Jardin, A., Jarvinen, A., Jaulmes, F., Jednorog, S., Jenkins, I., Jeong, C., Jepu, I., Joffrin, E., Johnson, R., Johnson, T., Johnston, Jane, Joita, L., Jones, G., Jones, T. T. C., Hoshino, K. K., Kallenbach, A., Kamiya, K., Kaniewski, J., Kantor, A., Kappatou, A., Karhunen, J., Karkinsky, D., Karnowska, I., Kaufman, M., Kaveney, G., Kazakov, Y., Kazantzidis, V., Keeling, D. L., Keenan, T., Keep, J., Kempenaars, M., Kennedy, C., Kenny, D., Kent, J., Kent, O. N., Khilkevich, E., Kim, H. T., Kim, H. S., Kinch, A., King, C., King, D., King, R. F., Kinna, D. J., Kiptily, V., Kirk, A., Kirov, K., Kirschner, A., Kizane, G., Klepper, C., Klix, A., Knight, P., Knipe, S. J., Knott, S., Kobuchi, T., Koechl, F., Kocsis, G., Kodeli, I., Kogan, L., Kogut, D., Koivuranta, S., Kominis, Y., Koeppen, M., Kos, B., Koskela, T., Koslowski, H. R., Koubiti, M., Kovari, M., Kowalska-Strzeciwilk, E., Krasilnikov, A., Krasilnikov, V., Krawczyk, N., Kresina, M., Krieger, K., Krivska, A., Kruezi, U., Ksiazek, I., Kukushkin, A., Kundu, A., Kurki-Suonio, T., Kwak, S., Kwiatkowski, R., Kwon, O. J., Laguardia, L., Lahtinen, A., Laing, A., Lam, N., Lambertz, H. T., Lane, C., Lang, P. T., Lanthaler, S., Lapins, J., Lasa, A., Last, J. R., Laszynska, E., Lawless, R., Lawson, A., Lawson, K. D., Lazaros, A., Lazzaro, E., Leddy, J., Lee, S., Lefebvre, X., Leggate, H. J., Lehmann, J., Lehnen, M., Leichtle, D., Leichuer, P., Leipold, F., Lengar, I., Lennholm, M., Lerche, E., Lescinskis, A., Lesnoj, S., Letellier, E., Leyland, M., Leysen, W., Li, L., Liang, Y., Likonen, J., Linke, J., Linsmeier, Ch., Lipschultz, B., Liu, G., Liu, Y., Lo Schiavo, V. P., Loarer, T., Loarte, A., Lobel, R. C., Lomanowski, B., Lomas, P. J., Lonnroth, J., Lopez, J. M., Lopez-Razola, J., Lorenzini, R., Losada, U., Lovell, J. J., Loving, A. B., Lowry, C., Luce, T., Lucock, R. M. A., Lukin, A., Luna, C., Lungaroni, M., Lungu, C. P., Lungu, M., Lunniss, A., Lupelli, I., Lyssoivan, A., Macdonald, N., Macheta, P., Maczewa, K., Magesh, B., Maget, P., Maggi, C., Maier, H., Mailloux, J., Makkonen, T., Makwana, R., Malaquias, A., Malizia, A., Manas, P., Manning, A., Manso, M. E., Mantica, P., Mantsinen, M., Manzanares, A., Maquet, Ph., Marandet, Y., Marcenko, N., Marchetto, C., Marchuk, O., Marinelli, M., Marinucci, M., Markovic, T., Marocco, D., Marot, L., Marren, C. A., Marshal, R., Martin, A., Martin, Y., Martin de Aguilera, A., Martinez, F. J., Martin-Solis, J. R., Martynova, Y., Maruyama, S., Masiello, A., Maslov, M., Matejcik, S., Mattei, M., Matthews, G. F., Maviglia, F., Mayer, M., Mayoral, M. L., May-Smith, T., Mazon, D., Mazzotta, C., McAdams, R., McCarthy, P. J., McClements, K. G., McCormack, O., McCullen, P. A., McDonald, D., McIntosh, S., McKean, R., McKehon, J., Meadows, R. C., Meakins, A., Medina, F., Medland, M., Medley, S., Meigh, S., Meigs, A. G., Meisl, G., Meitner, S., Meneses, L., Menmuir, S., Mergia, K., Merrigan, I. R., Mertens, Ph., Meshchaninov, S., Messiaen, A., Meyer, H., Mianowski, S., Michling, R., Middleton-Gear, D., Miettunen, J., Militello, F., Militello-Asp, E., Miloshevsky, G., Mink, F., Minucci, S., Miyoshi, Y., Mlynar, J., Molina, D., Monakhov, I., Moneti, M., Mooney, R., Moradi, S., Mordijck, S., Moreira, L., Moreno, R., Moro, F., Morris, A. W., Morris, J., Moser, L., Mosher, S., Moulton, D., Murari, A., Muraro, A., Murphy, S., Asakura, N. N., Na, Y. S., Nabais, F., Naish, R., Nakano, T., Nardon, E., Naulin, V., Nave, M. F. F., Nedzelski, I., Nemtsev, G., Nespoli, F., Neto, A., Neu, R., Neverov, V. S., Newman, M., Nicholls, K. J., Nicolas, T., Nielsen, A. H., Nielsen, P., Nilsson, E., Nishijima, D., Noble, C., Nocente, M., Nodwell, D., Nordlund, K., Nordman, H., Nouailletas, R., Nunes, I., Oberkofler, M., Odupitan, T., Ogawa, M. T., O'Gorman, T., Okabayashi, M., Olney, R., Omolayo, O., O'Mullane, M., Ongena, J., Orsitto, F., Orszagh, J., Oswuigwe, B. I., Otin, R., Owen, A., Paccagnella, R., Pace, N., Pacella, D., Packer, L. W., Page, A., Pajuste, E., Palazzo, S., Pamela, S., Panja, S., Papp, P., Paprok, R., Parail, V., Park, M., Diaz, F. Parra, Parsons, M., Pasqualotto, R., Patel, A., Pathak, S., Paton, D., Patten, H., Pau, A., Pawelec, E., Soldan, C. Paz, Peackoc, A., Pearson, I. J., Pehkonen, S. -P., Peluso, E., Penot, C., Pereira, A., Pereira, R., Puglia, P. P. Pereira, von Thun, C. Perez, Peruzzo, S., Peschanyi, S., Peterka, M., Petersson, P., Petravich, G., Petre, A., Petrella, N., Petrzilka, V., Peysson, Y., Pfefferle, D., Philipps, V., Pillon, M., Pintsuk, G., Piovesan, P., Pires dos Reis, A., Piron, L., Pironti, A., Pisano, F., Pitts, R., Pizzo, F., Plyusnin, V., Pomaro, N., Pompilian, O. G., Pool, P. J., Popovichev, S., Porfiri, M. T., Porosnicu, C., Porton, M., Possnert, G., Potzel, S., Powell, T., Pozzi, J., Prajapati, V., Prakash, R., Prestopino, G., Price, D., Price, M., Price, R., Prior, P., Proudfoot, R., Pucella, G., Puglia, P., Puiatti, M. E., Pulley, D., Purahoo, K., Puetterich, Th., Rachlew, E., Rack, M., Ragona, R., Rainford, M. S. J., Rakha, A., Ramogida, G., Ranjan, S., Rapson, C. J., Rasmussen, J. J., Rathod, K., Ratta, G., Ratynskaia, S., Ravera, G., Rayner, C., Rebai, M., Reece, D., Reed, A., Refy, D., Regan, B., Regana, J., Reich, M., Reid, N., Reimold, F., Reinhart, M., Reinke, M., Reiser, D., Rendell, D., Reux, C., Reyes Cortes, S. D. A., Reynolds, S., Riccardo, V., Richardson, N., Riddle, K., Rigamonti, D., Rimini, F. G., Risner, J., Riva, M., Roach, C., Robins, R. J., Robinson, S. A., Robinson, T., Robson, D. W., Roccella, R., Rodionov, R., Rodrigues, P., Rodriguez, J., Rohde, V., Romanelli, F., Romanelli, M., Romanelli, S., Romazanov, J., Rowe, S., Rubel, M., Rubinacci, G., Rubino, G., Ruchko, L., Ruiz, M., Ruset, C., Rzadkiewicz, J., Saarelma, S., Sabot, R., Safi, E., Sagar, P., Saibene, G., Saint-Laurent, F., Salewski, M., Salmi, A., Salmon, R., Salzedas, F., Samaddar, D., Samm, U., Sandiford, D., Santa, P., Santala, M. I. K., Santos, B., Santucci, A., Sartori, F., Sartori, R., Sauter, O., Scannell, R., Schlummer, T., Schmid, K., Schmidt, V., Schmuck, S., Schneider, M., Schoepf, K., Schworer, D., Scott, S. D., Sergienko, G., Sertoli, M., Shabbir, A., Sharapov, S. E., Shaw, A., Shaw, R., Sheikh, H., Shepherd, A., Shevelev, A., Shumack, A., Sias, G., Sibbald, M., Sieglin, B., Silburn, S., Silva, A., Silva, C., Simmons, P. A., Simpson, J., Simpson-Hutchinson, J., Sinha, A., Sipila, S. K., Sips, A. C. 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Subjects
causality detection, pacing, synchronization experiments, transfer entropy, elms, time series, mutual information, pearson correlation coefficient, pellets, sawteeth
Abstract

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.

19. On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals.

Author

Murari, Andrea, Lungaroni, Michele, Peluso, Emmanuele, Gaudio, Pasquale, Lerche, Ernesto, Garzotti, Luca, and Gelfusa, Michela

Subjects
*ENTROPY (Information theory), *TIME perspective, *PLASMA diagnostics, *PEARSON correlation (Statistics), *TIME series analysis
Abstract

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
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