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Overview of Modern Forecasting Methods and Models

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Overview of Modern Forecasting Methods and Models

International Conference on Management of Large-Scale System Development (MLSD)

作者： Vera Ivanyuk Anatoliy Tsvirkun Anna Sunchalina Tatiana Goroshnikova Andrey Sunchalin Galina Zholobova Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia Laboratory of Management of the Development of Large-Scale Systems V.A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Faculty of International Economic Relations Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods.

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The Nearly Universal Disk Galaxy Rotation Curve

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arXiv 2024年

作者： Patel, Raj Arora, Nikhil Courteau, Stephane Stone, Connor Frosst, Matthew Widrow, Lawrence Department of Physics Engineering Physics & Astronomy Queen’s University KingstonONK7L 3N6 Canada 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 ICRAR M468 University of Western Australia CrawleyWA6009 Australia

The Universal Rotation Curve (URC) of disk galaxies was originally proposed to predict the shape and amplitude of any rotation curve (RC) based solely on photometric data. Here, the URC is investigated with an extensive set of spatially-resolved rotation curvesdrawn from the PROBES-I, PROBES-II, and MaNGA data bases with matching multi-band surface brightness profiles from the DESI-LIS and WISE surveys for 3,846 disk galaxies. Common URC formulations fail to achieve an adequate level of accuracy to qualify as truly universal over fully sampled RCs. We develop neural network (NN) equivalents for the proposed URCs which predict RCs with higher accuracy, showing that URC inaccuracies are not due to insufficient data but rather non-optimal formulations or sampling effects. This conclusion remains even if the total RC sample is pruned for symmetry. The latest URC prescriptions and their NN equivalents trained on our sub-sample of 579 disk galaxies with symmetric RCs perform similarly to the URC/NN trained on the complete data sample. We conclude that a URC with an acceptable level of accuracy (∆Vcirc 15 per cent) at all radii would require a detailed modelling of a galaxy’s central regions and outskirts (e.g., for baryonic effects leading to contraction or expansion of any dark-matter-only halo). © 2024, CC BY.

关键词： Galaxies

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

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arXiv 2024年

作者： Stone, Connor Adam, Alexandre Coogan, Adam Yantovski-Barth, M.J. Filipp, Andreas Setiawan, Landung Core, Cordero Legin, Ronan Wilson, Charles Barco, Gabriel Missael Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela Institute Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila Québec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States eScience Institute Scientific Software Engineering Center 1410 NE Campus Pkwy SeattleWA98195 United States

Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are significant and multiple images of the background source are observed. The lens objects must align on the sky of order ∼ 1 arcsecond for galaxy-galaxy lensing, or 10’s of arcseonds for cluster-galaxy lensing. As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements and addressing critical questions in astrophysics. Notably, they facilitate the measurement of the Universe’s expansion rate (e.g. K. C. Wong et al., 2020), dark matter (e.g. Hezaveh et al., 2016;Vegetti & Vogelsberger, 2014), supernovae (e.g. Rodney et al., 2021), quasars (e.g. Peng et al., 2006), and the first stars (e.g. Welch et al., 2022) among other topics. With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must occur at orders of magnitude larger scale then ever before. Here we present caustics, a Python package designed to handle the extensive computational demands of modeling such a vast number of lensing systems. © 2024, CC BY.

关键词： Python

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

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arXiv 2024年

作者： Legin, Ronan Isi, Maximiliano Wong, Kaze W.K. Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila - Quebec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Trottier Space Institute MontréalQC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to "clean" non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations;alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade. © 2024, CC BY.

关键词： Background noise

Fractal Interpolation Densities

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SSRN

SSRN 2023年

作者： Igudesman, Konstantin Tumakov, Maksim Snegina, Svetlana Tumakov, Dmitrii Department of Data Analysis and Machine Learning Financial University under the Government of Russian Federation 4th Veshnyakovsky pr. 4 Moscow111395 Russia Centre for Econometrics and Business Analytics St. Petersburg State University Uni-versitetskaya nab. 7/9 St. Petersburg199034 Russia Kazan Federal University Institute of Computational Mathematics and Information Technologies Russia Department of Legal Regulation of Economic Operations University under the Government of Russian Federation 4th Veshnyakovsky pr. 4 Moscow111395 Russia

Necessary and sufficient conditions are found under which a continuous fractal interpolation function is the distribution density of a continuous random variable. Examples of fractal interpolation densities are consid... 详细信息

关键词： Interpolation

E-service quality from attributes to outcomes: The similarity and difference between digital and hybrid services

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Journal of Open Innovation: Technology, Market, and Complexity 2020年第4期6卷 1-21页

作者： Vatolkina, Natalia Gorbashko, Elena Kamynina, Nadezhda Fedotkina, Olga Department of Management Bauman Moscow State Technical University (National Research University) Moscow 105005 Russian Federation Department of Project and Quality Management Saint Petersburg State University of Economics St. Petersburg 191023 Russian Federation Department of Land Law and State Registration of Real Estate Moscow State University of Geodesy and Cartography Moscow 105064 Russian Federation Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow 125167 Russian Federation

Our research goal is to offer an e-service quality model based on experience and multidimensional quality and compare its applicability for e-services to find differences and similarities in consumer perceptions and behavioral intentions. Additionally, we seek to compare attributes that compose quality dimensions for hybrid and digital e-services. The study was based on an online survey conducted in July–September 2019 among citizens and foreign residents in the Russian Federation. Respondents had to answer questions concerning a specific e-service brand to capture real consumer behavior. The data of 365 questionnaires were analyzed using the Spearman correlation to determine the relationship between the model components. Customer experience is a valid outcome variable in the e-service model that strongly influences customer satisfaction and repurchase intentions. The model proved to be equally valid for hybrid and digital e-services. The key differences between digital and hybrid e-services lie within the distribution of e-service attributes between quality dimensions. Ease of use and perceived usefulness are the most essential attributes that have a direct influence on customer satisfaction. The findings show the necessity of best practices diffused between different types of e-services and present an opportunity to widely spread research findings between different e-service sectors. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Customer experience E-service E-service quality

The Intrinsic Flattening of Extragalactic Stellar Disks

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arXiv 2024年

作者： Favaro, Jeremy Courteau, Stephane Comerón, Sébastien Stone, Connor Department of Physics Engineering Physics & Astronomy Queen’s University KingstonONK7L 3N6 Canada Departamento de Astrofísica Universidad de La Laguna Tenerife La Laguna38200 Spain Instituto de Astrofísica de Canarias Tenerife La Laguna38205 Spain 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

Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, c/a, where c and a are respectively the vertical and long radial axes of the disk measured at suitable stellar densities. The ratio c/a is a necessary quantity in the assessment of galaxy inclinations, three-dimensional structural reconstructions, total masses, as well as a constraint to galaxy formation models. 3.6 µm maps of 133 edge-on spiral galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S4G) and its early-type galaxy extension are used to revisit the assessment of c/a free from dust extinction and away from the influence of a stellar bulge. We present a simple definition of c/a and explore trends with other galactic physical parameters: total stellar mass, concentration index, total H i mass, mass of the central mass concentration, circular velocity, model-dependent scales, as well as Hubble type. Other than a dependence on early/late Hubble types, and a related trend with light concentration, no other parameters were found to correlate with the intrinsic flattening of spiral galaxies. The latter is mostly constant with 〈c/a〉 = 0.124 ±0.001 (stat) ±0.033 (intrinsic/systematic) and greater for earlier types. © 2024, CC BY.

关键词： Galaxies

Development of Modern Forecasting Models

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Development of Modern Forecasting Models

International Conference on Management of Large-Scale System Development (MLSD)

作者： Vera Ivanyuk Konstantin Shuvalov Gurami Akhobadze Victoria Malekova Alexey Mikhailov Kiril Levchenko Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia Department of Higher Mathematics Bauman Moscow State Technical University Moscow Russia Laboratory of Management of Large-Scale System Development V.A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Laboratory of Network Systems Control V. A.Trapeznikov Institute of Control Sciences of RAS Moscow Russia Department of Financial Markets and Financial Engineering Financial University Under the Government of the Russian Federation Moscow Russia Department of Mathematics Financial University Under the Government of the Russian Federation Moscow Russia

The paper analyzed ensemble forecasting models. The results showed that ensemble models can improve forecasting performance by compensating for the shortcomings of weak models.

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Ndmfcs: An Automatic Fruit Counting System in Modern Apple Orchard Using Abatement of Abnormal Fruit Detection

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SSRN

SSRN 2023年

作者： Wu, Zhenchao Sun, Xiaoming Jiang, Hanhui Mao, Wulan Li, Rui Andriyanov, Nikita Soloviev, Vladimir Fu, Longsheng College of Mechanical and Electronic Engineering Northwest A&F University Shaanxi Yangling712100 China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Shaanxi Yangling712100 China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Shaanxi Yangling712100 China Northwest A&F University Shenzhen Research Institute Guangdong Shenzhen518000 China Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow125167 Russia Institute of Agricultural Mechanization Xinjiang Academy of Agricultural Sciences Urumqi830000 China

Automatic fruit counting is an important task for growers to estimate yield and manage orchards. Although many deep-learning-based fruit detection algorithms have been developed to improve performance of automatic fruit counting systems, abnormal fruit detection has often been caused by these algorithms detecting non-target fruits that have similar growth characteristics to target fruits. Abnormal fruit detection in modern apple orchard, i.e. detected fruits in the back row of trees (DFBRT) or on the ground (DFG), would result in a higher number of fruits counting than the ground truth. This study proposes an automatic fruit counting system called NDMFCS (Normal Detection Matched Fruit Counting System) to solve this problem for improving fruit counting accuracy in modern apple orchard. NDMFCS consists of three sub-systems, i.e. object detection based on You Only Look Once Version 4-tiny (YOLOv4-tiny), abatement of abnormal fruit detection based on threshold, and fruit counting based on trunk tracking and ID assignment. YOLOv4-tiny was selected to implement detection of fruits and trunks, whose output is confidence and pixel coordinates of detected object. The DFBRT and the DFG were defined as abnormal fruit detection and abated by thresholds to improve detection performance of fruit. Detected fruits were removed when their distance from camera is further than a distance threshold (defined as the DFBRT) or the confidence of fruit detection is less than a confidence threshold (defined as the DFG). Finally, fruit counting was implemented by trunk tracking and ID assignment, where each fruit was assigned a unique tracking ID. Results on 10 sets of original videos indicated that average fruit detection precision was improved from 89.1% to 93.3% after abatement of abnormal fruit detection. Also, Multiple Object Tracking Accuracy and Multiple Object Tracking Precision were improved on average by 4.2% and 3.3%, respectively, while average ID Switch Rate was decreased on ave

关键词： Object detection