Academic literature on the topic 'Path signatures'
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Journal articles on the topic "Path signatures"
Kalsi, Jasdeep, Terry Lyons, and Imanol Perez Arribas. "Optimal Execution with Rough Path Signatures." SIAM Journal on Financial Mathematics 11, no. 2 (January 2020): 470–93. http://dx.doi.org/10.1137/19m1259778.
Full textCartea, Álvaro, Imanol Pérez Arribas, and Leandro Sánchez-Betancourt. "Double-Execution Strategies Using Path Signatures." SIAM Journal on Financial Mathematics 13, no. 4 (November 21, 2022): 1379–417. http://dx.doi.org/10.1137/21m1456467.
Full textBoland, Philip J. "Signatures of indirect majority systems." Journal of Applied Probability 38, no. 2 (June 2001): 597–603. http://dx.doi.org/10.1239/jap/996986765.
Full textBoland, Philip J. "Signatures of indirect majority systems." Journal of Applied Probability 38, no. 02 (June 2001): 597–603. http://dx.doi.org/10.1017/s0021900200020064.
Full textFang, Yuan, Zhe Su, Jing Xie, Ruidong Xue, Qi Ma, Yanmeng Li, Yifan Zhao, et al. "Genomic signatures of pancreatic adenosquamous carcinoma (PASC)." Journal of Pathology 243, no. 2 (September 5, 2017): 155–59. http://dx.doi.org/10.1002/path.4943.
Full textJiang, Yunjiang, and Weijun Xu. "On the Number of Turns in Reduced Random Lattice Paths." Journal of Applied Probability 50, no. 2 (June 2013): 499–515. http://dx.doi.org/10.1239/jap/1371648957.
Full textJiang, Yunjiang, and Weijun Xu. "On the Number of Turns in Reduced Random Lattice Paths." Journal of Applied Probability 50, no. 02 (June 2013): 499–515. http://dx.doi.org/10.1017/s0021900200013528.
Full textShestakova, Tatyana. "On A. D. Sakharov’s Hypothesis of Cosmological Transitions with Changes in the Signature of the Metric." Universe 7, no. 5 (May 17, 2021): 151. http://dx.doi.org/10.3390/universe7050151.
Full textWebster, Jonathan A., Andrew H. Beck, Mimansa Sharma, Inigo Espinosa, Britta Weigelt, Marthe Schreuder, Kelli D. Montgomery, Kristin C. Jensen, Matt van de Rijn, and Robert West. "Variations in stromal signatures in breast and colorectal cancer metastases." Journal of Pathology 222, no. 2 (May 21, 2010): 158–65. http://dx.doi.org/10.1002/path.2738.
Full textKumar, Pankaj, Saurabh Kumar Sharma, and Kaveri Umesh Kadam. "Prolong the lifetime of sensor monitoring system using schedule matrix while employing digital signatures." Journal of Information and Optimization Sciences 44, no. 7 (2023): 1327–46. http://dx.doi.org/10.47974/jios-1286.
Full textDissertations / Theses on the topic "Path signatures"
Chevyrev, Ilya. "Characteristic functions of path signatures and applications." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:3dd5e063-bde0-434f-a781-61d3fe22aaa1.
Full textBoedihardjo, Horatio S. "Signatures of Gaussian processes and SLE curves." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:5f835640-d3f5-4b03-b600-10d897644ced.
Full textInzirillo, Hugo. "Contributions to Econometric and Deep Learning Methods for Time Series Forecasting." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAG005.
Full textThis thesis, structured in four distinct parts, contributes to enriching the fields of deep learning and nonlinear econometrics. Traditional models have often shown weaknesses in capturing the nonlinear behaviors and regime shifts characteristic of digital assets. This thesis focuses on improving the prediction of returns and volatility of this new asset class through the development of new neural network architectures and innovative methodologies. In a first part, we introduce Temporal Kolmogorov-Arnold Networks (TKANs) as well as a new transformer-based architecture (TKAT) based on TKANs for time series forecasting. These models integrate memory management and attention mechanism to capture the complex dynamics of financial markets. Our results show that TKAN and TKAT outperform traditional recurrent models (LSTM, GRU) in terms of stability and prediction accuracy, particularly over longer time horizons. The second part proposes a new approach for modeling market regimes using recurrent neural networks and new Markov-Switching GARCH dynamics. This former approach allows to detect market regimes and estimate transition probabilities between different states. The results show a significant improvement in the detection and prediction of market regimes compared to traditional models.The latter models define new meaningful MS-GARCH specifications and convenient simulation-based estimations procedures.In the third part, we explore the use of signatures for portfolio construction. A signature-based classification method is developed to select the most representative assets of each cluster, thus improving the risk-return ratio. Portfolios built with this methodology show superior performances compared to standard portfolios. The last part of this thesis addresses volatility modeling and anomaly detection. We propose a new LSTM layer, the Attention Free Long Short-Term Memory (AF-LSTM), for volatility prediction. This new layer shows superior predictive performances compared to the original version. For anomaly detection, a conditional autoencoder (AF-CAE) is introduced, providing better anomaly detection, reducing false positives. This thesis proposes several contributions in the field of time series prediction; the results demonstrate improvements in terms of accuracy and robustness, opening new perspectives for the modeling and management of financial assets
Xu, Weijun. "Inverting the signature of a path." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:954ff1e3-9162-456a-91a3-39734854cde2.
Full textGeng, Xi. "The signature of a rough path : uniqueness." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:f15c0439-2b30-4738-9eab-0dffd86bed69.
Full textNi, Hao. "The expected signature of a stochastic process." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:e0b9e045-4c09-4cb7-ace9-46c4984f16f6.
Full textJanssen, Arend. "Order book models, signatures and numerical approximations of rough differential equations." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:264e96b3-f449-401b-8768-337acab59cab.
Full textHabermann, Karen. "Geometry of sub-Riemannian diffusion processes." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/271855.
Full textFaye, Mbaye. "Signature infrarouge et modélisation pour la télédétection de deux gaz : SF6 et RuO4." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS182.
Full textThis work is a contribution to the spectroscopic study of two gases of anthropogenic origin for which the modeling of the infrared signature may allow a quantification in releases in the atmosphere. Sulfur hexafluoride (SF₆) is mainly rejected by the electrical industry, which uses it as a gaseous insulator and its concentration increases rapidly in the atmosphere (of the order of 7 per cent per year). It is a potent greenhouse gas listed in the Kyoto Protocol because its intense absorption around 10 μm issituated in a window of atmospheric transparency and its lifetime in the upper atmosphere (approximately 3200 years) confers an exceptionally high radiative transfer coefficient (Global WarmingPotential, or GWP equal to 23900 times that of carbon dioxide,CO2). The detection and quantification of this gas by its infrared signature via satellite measurements requires a very accurate modeling of the absorption spectra, incompletely known. For SF₆,in particular because of the fact that this heavy molecule presents a large number of vibrational levels of low energy, there exist hotbands in the region of atmospheric absorption around its most intense fundamental absorption (v₃); these involve excited states little or not characterized. Thus, at ambient temperature, only 32% of the molecules are located in the vibrational ground state. Current databases contain only data for the fundamental vibrationband. To compensate for this lack, it is therefore necessary toobserve a number of transitions from the ground state to the excited levels of low energy used corresponding to initial states of the hot bands and to acquire data for modeling also levels with two quanta of vibration constituting the upper levels of arrival of thes ehot bands.This work is a contribution to the spectroscopic study of two gases of anthropogenic origin for which the modeling of the infrared signature may allow a quantification in releases in the atmosphere.Sulfur hexafluoride (SF₆) is mainly rejected by the electricalindustry, which uses it as a gaseous insulator and its concentration increases rapidly in the atmosphere (of the order of 7 per cent peryear). It is a potent greenhouse gas listed in the Kyoto Protocolbecause its intense absorption around 10 μm is situated in awindow of atmospheric transparency and its lifetime in the upper atmosphere (approximately 3200 years) confers an exceptionally high radiative transfer coefficient (Global Warming Potential, orGWP equal to 23900 times that of carbon dioxide, CO2). The detection and quantification of this gas by its infrared signature via satellite measurements requires a very accurate modeling of theabsorption spectra, incompletely known. For SF6, in particular because of the fact that this heavy molecule presents a largenumber of vibrational levels of low energy, there exist hot bands in the region of atmospheric absorption around its most intensefundamental absorption (v3); these involve excited states little or not characterized. Thus, at ambient temperature, only 32 % of themolecules are located in the vibrational ground state. Current databases contain only data for the fundamental vibration band. Tocompensate for this lack, it is therefore necessary to observe a number of transitions from the ground state to the excited levels oflow energy used corresponding to initial states of the hot bands andto acquire data for modeling also levels with two quanta ofvibration constituting the upper levels of arrival of these hot bands
Kebichi, Omar. "Techniques et outils de CAO pour la génération automatique de test intégré pour RAMs." Grenoble INPG, 1994. http://www.theses.fr/1994INPG0062.
Full textBooks on the topic "Path signatures"
Chien-ming, Chin, University of Illinois at Urbana-Champaign. Electromagnetic Communication Laboratory., and United States. National Aeronautics and Space Administration., eds. Electromagnetic scattering from realistic targets: Final report for NASA NAG 3-1474. Urbana, IL: Electromagnetics Laboratory, Dept. of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 1997.
Find full textBellman, Geoffrey M. Your Signature Path: Gaining New Perspectives on Life and Work. Berrett-Koehler Publishers, Incorporated, 1996.
Find full textYour signature path: Gaining new perspectives on life and work. San Francisco: Berrett-Koehler Publishers, 1996.
Find full textDiscovering your soul signature: A 33-day path to purpose, passion, & joy. 2014.
Find full textMatrix, The: Path of Neo(tm) Official Strategy Guide (Bradygames Signature Series Guide). BRADY GAMES, 2005.
Find full textLatour, Melinda. Santana and the Metaphysics of Tone. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199985227.003.0010.
Full textKitses, Jim. Horizons West. Bloomsbury Publishing Plc, 2007. http://dx.doi.org/10.5040/9781838711276.
Full textLiechty, Edward A., and Martin M. Fisher, eds. Coding for Pediatrics, 2016. 21st ed. American Academy of Pediatrics, 2015. http://dx.doi.org/10.1542/9781581109597.
Full textLiechty, Edward A., Cindy Hughes, and Becky Dolan, eds. Coding for Pediatrics, 2014. American Academy of Pediatrics, 2013. http://dx.doi.org/10.1542/9781581108354.
Full textBook chapters on the topic "Path signatures"
Wang, Chenyang, Ling Luo, and Uwe Aickelin. "Quasi-Periodicity Detection via Repetition Invariance of Path Signatures." In Advances in Knowledge Discovery and Data Mining, 301–13. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_24.
Full textNeginhal, Mradula, Khaled Harfoush, and Harry Perros. "Measuring Bandwidth Signatures of Network Paths." In NETWORKING 2007. Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet, 1072–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72606-7_92.
Full textHuang, Zi, Xiaofang Zhou, Heng Tao Shen, and Dawei Song. "3D Protein Structure Matching by Patch Signatures." In Lecture Notes in Computer Science, 528–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11827405_52.
Full textOliveira, Dario Augusto Borges, and Matheus Palhares Viana. "Using 1D Patch-Based Signatures for Efficient Cascaded Classification of Lung Nodules." In Patch-Based Techniques in Medical Imaging, 67–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00500-9_8.
Full textFermanian, Adeline, Jiawei Chang, Terry Lyons, and Gérard Biau. "The Insertion Method to Invert the Signature of a Path." In Recent Advances in Econometrics and Statistics, 575–95. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61853-6_29.
Full textSong, Sung Keun, Hee Yong Youn, and Kang Shin Lee. "A New Digital Signature and Certificate Architecture with Shortest Certification Path." In Lecture Notes in Computer Science, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24693-0_1.
Full textLy Van, B., S. Garcia-Salicetti, and B. Dorizzi. "Fusion of HMM’s Likelihood and Viterbi Path for On-line Signature Verification." In Biometric Authentication, 318–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25976-3_29.
Full textZhang, Xin, Jiale Cheng, Hao Ni, Chenyang Li, Xiangmin Xu, Zhengwang Wu, Li Wang, Weili Lin, Dinggang Shen, and Gang Li. "Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 134–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59728-3_14.
Full textShin, Hakgene, Heeju Lee, and Jaewoo Chang. "A Concatenated Signature Scheme on Path Dictionary for Query Processing of Composite Objects." In OOIS’97, 44–54. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1525-0_5.
Full textIgnatidis, Panagiotis, Henrik von der Haar, Christoph Hennecke, and Friedrich Dinkelacker. "Impact of Mixing on the Signature of Combustor Defects." In Regeneration of Complex Capital Goods, 95–113. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-51395-4_6.
Full textConference papers on the topic "Path signatures"
Oppeneer, Martien, Piet Bijl, Judith Dijk, Miranda van Iersel, and Johan-Martijn ten Hove. "Target acquisition modeling over the exact optical path: extending the EOSTAR TDA with the TOD sensor performance model." In Target and Background Signatures, edited by Karin U. Stein and Ric Schleijpen. SPIE, 2017. http://dx.doi.org/10.1117/12.2278848.
Full textMorley, Sam, and Terry Lyons. "RoughPy." In Python in Science Conference. SciPy, 2024. http://dx.doi.org/10.25080/dxwy3560.
Full textMoore, Paul, Theodor-Mihai Iliant, Filip-Alexandru Ion, Yue Wu, and Terry Lyons. "Path Signatures for Non-Intrusive Load Monitoring." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747285.
Full textZhang, Ze, Sunghyun Park, and Scott Mahlke. "Path Sensitive Signatures for Control Flow Error Detection." In LCTES '20: 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3372799.3394360.
Full textWilson, David J., R. Burt Dedmon, W. W. Montgomery, and C. E. Craven. "Doppler Signatures of Hard Targets." In Coherent Laser Radar. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/clr.1987.tha4.
Full textLoch, Adrian, David Meier, and Matthias Hollick. "How did you get here? PHY-layer path signatures." In 2014 IEEE 15th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM). IEEE, 2014. http://dx.doi.org/10.1109/wowmom.2014.6919019.
Full textFlanigan, Edward, Arkan Abdulrahman, and Spyros Tragoudas. "Sequential Path Delay Fault Identification Using Encoded Delay Propagation Signatures." In 2008 9th International Symposium of Quality of Electronic Design (ISQED). IEEE, 2008. http://dx.doi.org/10.1109/isqed.2008.4479811.
Full textChen, Yangjun, Yong Shi, and Yibin Chen. "Tree inclusion algorithm, signatures and evaluation of path-oriented queries." In the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141521.
Full textHaderlein, Jonas F., Andre D. H. Peterson, Parvin Zarei Eskikand, Mark J. Cook, Anthony N. Burkitt, David B. Grayden, and Iven M. Y. Mareels. "Revisiting Seizure Prediction with Path Signatures: the Limitations of System Identification." In 2024 Australian & New Zealand Control Conference (ANZCC). IEEE, 2024. http://dx.doi.org/10.1109/anzcc59813.2024.10432829.
Full textVarma, Nakul, Sujit Jadhav, Kumar Manish, Ravi Chandak, Avdesh Negi, Ajay Jha, and Avinash Bohra. "Horizontal Sucker Rod Pumping Wells – Novel Unconventional Dyna Card Signatures Interpretation for Pump & Rod Run-life Optimisation." In Asia Pacific Unconventional Resources Symposium. SPE, 2023. http://dx.doi.org/10.2118/217279-ms.
Full textReports on the topic "Path signatures"
Simandl, G. J., R. J. D'Souza, S. Paradis, and J. Spence. Rare-earth element content of carbonate minerals in sediment-hosted Pb-Zn deposits, southern Canadian Rocky Mountains. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328001.
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