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Forecasting the dynamics of financial time series based on neural networks

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk N M Abdikeev A D Tsvirkun Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

Forecasting is one of the high-demand data mining problems, but also a very difficult one. The difficulties of forecasting are associated with insufficient quality and quantity of input data, the changes in the environment where the process takes place, and the impact of subjective factors. A forecast always implies some margin of error, which depends on the forecast model used and the completeness of the input data. Methods based on neural networks are the most relevant and highly-demanded techniques today. Neural networks are great for finding accurate solutions in an environment characterized by complex or fragmented information. In the field of finance and economics, the values of time series parameters can be more accurately modelled using neural analysis methods. Artificial neural networks have more common and flexible functional forms than statistical methods. They can generalize information and provide a qualitative forecast under conditions of uncertainty and crisis. The article proposes a forecasting model based on a neural network that can predict the price of a financial asset in a well-defined time interval. Ten technical indicators are used as input signals, and the closing price of the next period is used as an output signal.

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Ensemble forecasting method

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Journal of Physics: Conference Series 2020年第1期1703卷

作者： V A Ivanyuk A D Tsvirkun Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt 125993 Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

The purpose of this article is to analyze the time series based on aggregate forecasting methods. Forecasting time series comprises an important scientific and technical task which is relevant in various sectors of economy and production. The main objective of any forecasting process is to get the ability to assess trends in changes to a particular factor. To predict the behaviour of future processes, a qualitative analysis of data is required, which provides the basis for the forecast. It is also important to choose the appropriate forecasting method. The forecast obtained may not be always correct, so it is crucial to determine how accurately it is built. In the present paper, characteristics of time series are provided. The main mathematical methods of time series forecasting are analyzed and their classification is presented. Criteria for the accuracy and reliability of forecasting models are defined. The accuracy of the forecast is an important criterion for evaluating the forecasting method. The aggregate ensemble forecast has proven to be an effective method for enhancing the accuracy of forecasting. The article provides an example of constructing an aggregate ensemble forecast. The types of nonlinear models are described and several methods are selected for constructing the aggregate ensemble forecast. The practical application of exponential smoothing is considered, the MA (moving average) model and the AR (autoregressive) model are constructed.

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AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

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arXiv 2023年

作者： Stone, Connor J. Courteau, Stéphane Cuillandre, Jean-Charles Hezaveh, Yashar Perreault-Levasseur, Laurence Arora, Nikhil Department of Physics Université de Montréal MontréalQC Canada Mila - Québec Artificial Intelligence Institute MontréalQC Canada Ciela - Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Engineering Physics & Astronomy Queen’s University KingstonON Canada AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-YvetteF-91191 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates New York University Abu Dhabi United Arab Emirates

We present ASTROPHOT, a fast, powerful, and user-friendly Python based astronomical image photometry solver. ASTROPHOT incorporates automatic differentiation and GPU (or parallel CPU) acceleration, powered by the machine learning library PYTORCH. Everything: ASTROPHOT can fit models for sky, stars, galaxies, PSFs, and more in a principled X2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: ASTROPHOT can optimize forward models on CPU or GPU;across images that are large, multiband, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg-Marquardt, Gradient descent, and No-U-Turn MCMC sampling. With an object-oriented user interface, ASTROPHOT makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the ASTROPHOT code and compares it to other popular astronomical image modeling software. ASTROPHOT is open-source, fully Python based, and freely accessible here: https://***/Autostronomy/AstroPhot © 2023, CC BY.

关键词： Python

The Cosmic Microwave Background and H0

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arXiv 2023年

作者： Lemos, Pablo Shah, Paul Mila - Quebec Artificial Intelligence Institute Montréal 6666 Rue Saint-Urbain QCH2S 3H1 Canada Department of Physics Université de Montréal Montréal 1375 Avenue Thérèse-Lavoie-Roux QCH2V 0B3 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

The cosmic microwave background (CMB) offers a unique window into the earlyuniverse, providing insights into cosmological parameters like the Hubbleconstant. Recent precise measurements of the CMB by experiments like Planckseem to point to a lower value for the Hubble constant compared to some othermeasurements like those from Type Ia supernovae. This discrepancy, known as theHubble tension, currently lacks a definitive explanation. In this chapter, weprovide an overview of how the Hubble constant is determined from detailedmeasurements of the CMB power spectrum. We explain the physics underlying keyfeatures of the CMB spectrum and their connection to cosmological *** then examine the consistency of Planck\'s Hubble constant determination, bothinternally within the data itself and externally with other astrophysicalprobes. While largely consistent, some anomalies like the lensing amplitudeparameter $A_L$ remain unresolved. We also explore various theoreticalextensions to the standard ${\\Lambda}$CDM cosmological model and assess theirpotential to resolve the Hubble tension. No clear resolution emerges,indicating significant tensions remain between early and late universe probeswithin simple extensions to ${\\Lambda}$CDM. Upcoming CMB experiments promiseimproved precision and should provide further insights into this cosmicconundrum. A coherent picture bridging measurements across cosmic time remainsan open challenge at the forefront of modern cosmology. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

The dark balance: quantifying the inner halo response to active galactic nuclei feedback in galaxies

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arXiv 2024年

作者： Arora, Nikhil Courteau, Stéphane Macciò, Andrea V. Cho, Changhyun Patel, Raj Stone, Connor New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates New York University Abu Dhabi Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Department of Physics Engineering Physics & Astronomy Queen’s University KingstonONK7L 3N6 Canada Max-Planck-Institut für Astronomie Königstuhl 17 HeidelbergD-69117 Germany Department of Physics Université de Montréal MontréalQC Canada Mila - Québec Artificial Intelligence Institute MontréalQC Canada Ciela - Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada

This paper presents a study of the impact of supermassive black hole (SMBH) feedback on dark matter (DM) halos in numerical NIHAO simulations of galaxies. In particular, the amount of DM displaced via active galactic nuclei (AGN) feedback and the physical scale over which AGN feedback affects the DM halo are quantified by comparing NIHAO simulations with and without AGN feedback. NIHAO galaxies with log(M∗/M☉) ≥ 10.0 show a growing central DM suppression of 0.2 dex (∼40 per cent) from z = 1.5 to the present relative to noAGN feedback simulations. The growth of the DM suppression is related to the mass evolution of the SMBH and the gas mass in the central regions. For the most massive NIHAO galaxies log(M∗/M☉) > 10.5, partially affected by numerical resolution, the central DM suppression peaks at z = 0.5, after which halo contraction overpowers AGN feedback due a shortage of gas and, thus, SMBH growth. The spatial scale, or "sphere of influence," over which AGN feedback affects the DM distribution decreases as a function of time for MW-mass galaxies (from ∼16 kpc at z = 1.5 to ∼7.8 kpc at z = 0) as a result of halo contraction due to stellar growth. For the most massive NIHAO galaxies, the size of the sphere of influence remains constant (∼16 kpc) for z > 0.5 owing to the balance between AGN feedback and halo contraction. © 2024, CC BY.

关键词： Active Galactic Nuclei

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

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arXiv 2023年

作者： Legin, Ronan Ho, Matthew Lemos, Pablo Perreault-Levasseur, Laurence Ho, Shirley Hezaveh, Yashar Wandelt, Benjamin Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Sorbonne Université CNRS UMR 7095 Institut d’Astrophysique de Paris 98 bis bd Arago Paris75014 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Sorbonne Université Institut Lagrange de Paris 98 bis boulevard Arago Paris75014 France

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods. © 2023, CC BY.

关键词： Sampling

Simulation-guided galaxy evolution inference: A case study with strong lensing galaxies

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arXiv 2023年

作者： Filipp, Andreas Shu, Yiping Pakmor, Rüdiger Suyu, Sherry H. Huang, Xiaosheng Technical University Munich TUM School of Natural Sciences Physics Department James-Franck-Strasse 1 Garching85748 Germany Karl-Schwarzschlid-Strasse 1 Garching85748 Germany Université de Montréal Physics Department 1375 Ave.Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China 11F of ASMAB No.1 Section 4 Roosevelt Road Taipei10617 Taiwan University of San Francisco 2130 Fulton Street San FranciscoCA94117-1080 United States

Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy and dark matter, baryonic physics, and more. It is, however, infeasible to track the evolutionary processes of individual galaxies in real time given their long timescales. As a result, galaxy evolution analyses have been mostly based on ensembles of galaxies that are supposed to be from the same population according to usually basic and crude observational criteria. We propose a new strategy of evaluating the evolution of an individual galaxy by identifying its descendant galaxies as guided by cosmological simulations. As a proof of concept, we examined the evolution of the total mass distribution of a target strong lensing galaxy at z = 0.884 using the proposed strategy. We selected 158 galaxies from the IllustrisTNG300 simulation that we identified as analogs of the target galaxy. We followed their descendants and found 11 observed strong lensing galaxies that match in stellar mass and size with the descendants at their redshifts. The observed and simulated results are discussed, although no conclusive assessment is made given the low statistical significance due to the small sample size. Nevertheless, the test confirms that our proposed strategy is already feasible with existing data and simulations. We expect it to play an even more important role in studying galaxy evolution as more strong lens systems and larger simulations become available with the advent of next-generation survey programs and cosmological simulations. © 2023, CC BY-NC-ND.

关键词： Galaxies

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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