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Grain Price Forecasting Using a Hybrid stochastic Method

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2017年第5期34卷 1750020-1750020页

作者： Zhao, Yu Zhang, Xi Shi, Zhongshun He, Lei Peking Univ Dept Ind Engn & Management Beijing 100871 Peoples R China Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China's national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series.

关键词： stochastic algorithm neural networks wavelet decomposition price forecasting stochastic process

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A Constrained Metropolis-Hastings Robbins-Monro algorithm for Q Matrix Estimation in DINA Models

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PSYCHOMETRIKA 2020年第2期85卷 322-357页

作者： Liu, Chen-Wei Andersson, Bjorn Skrondal, Anders Natl Taiwan Normal Univ Taipei Taiwan Univ Oslo Oslo Norway Norwegian Inst Publ Hlth Oslo Norway Univ Calif Berkeley Berkeley CA 94720 USA

In diagnostic classification models (DCMs), theQmatrix encodes in which attributes are required for each item. TheQmatrix is usually predetermined by the researcher but may in practice be misspecified which yields incorrect statistical inference. Instead of using a predeterminedQmatrix, it is possible to estimate it simultaneously with the item and structural parameters of the DCM. Unfortunately, current methods are computationally intensive when there are many attributes and items. In addition, the identification constraints necessary for DCMs are not always enforced in the estimation algorithms which can lead to non-identified models being considered. We address these problems by simultaneously estimating the item, structural andQmatrix parameters of the Deterministic Input Noisy "And" gate model using a constrained Metropolis-Hastings Robbins-Monro algorithm. Simulations show that the new method is computationally efficient and can outperform previously proposed Bayesian Markov chain Monte-Carlo algorithms in terms ofQmatrix recovery, and item and structural parameter estimation. We also illustrate our approach using Tatsuoka's fraction-subtraction data and Certificate of Proficiency in English data.

关键词： Diagnostic classification models Qmatrix stochastic algorithm

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stochastic Local Community Detection in Networks

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algorithmS 2023年第1期16卷 22-22页

作者： Papei, Hadi Li, Yang Univ Western Ontario Dept Phys & Astron London ON 637 Canada Florida Atlantic Univ Dept Math Sci Boca Raton FL 33431 USA

We propose a stochastic agglomerative algorithm to detect the local community of some given seed vertex/vertices in a network. Instead of giving a deterministic binary local community in the output, our method assigns every vertex a value that is the probability that this particular vertex would be in the local community of the seed. The proposed procedure has several advantages over the existing deterministic algorithms, including avoiding random tie-breaking, evaluating uncertainties, detecting hierarchical community structure, etc. Synthetic and real data examples are included for illustration.

关键词： community stochastic algorithm network science simulated annealing

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Maiden application of an sine-cosine algorithm optimised FO cascade controller in automatic generation control of multi-area thermal system incorporating dish-Stirling solar and geothermal power plants

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IET RENEWABLE POWER GENERATION 2018年第5期12卷 585-597页

作者： Tasnin, Washima Saikia, Lalit C. Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

The present study highlights an attempt of integrating the geothermal power plant (GTPP) in automatic generation control of an interconnected system comprising of dish-Stirling solar-thermal system (DSTS) and the conventional thermal system (TS). Generation rate constraints of 3%/min are considered for the TSs. A new fractional-order (FO) cascade controller named as FO proportional (P)-integral (I)-FOP-derivative (D) (FOPI-FOPD) is proposed as secondary controller and performance is compared with commonly used classical controllers. Controller gains and other parameters are optimised using a novel stochastic algorithm called sine-cosine algorithm. The analysis reveals the superiority of FOPI-FOPD over others. The effect of inclusion of GTPP and DSTS is also analysed on the conventional TS, both in a combined manner and separately. Sensitivity analysis reflects the robustness of optimum FOPI-FOPD controller gains and other parameters obtained at nominal and recommend that the optimised parameters do not suffer much deviations and are able to withstand wide fluctuations in system operating conditions, system loading and inertia constant. The dynamic behaviour of the system is studied with 1% step load perturbation in area1.

关键词： geothermal power stations solar power stations PI control power generation control cascade control stochastic processes sine-cosine algorithm FO cascade controller automatic generation control multiarea thermal system dish-Stirling solar power plants geothermal power plants GTPP interconnected system inertia constant system loading system operating conditions FOPI-FOPD controller gains stochastic algorithm secondary controller fractional-order cascade controller generation rate constraints DSTS dish-Stirling solar-thermal system

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Can Gauss-Newton algorithms Outperform stochastic Optimization algorithms When Calibrating a Highly Parameterized Hydrological Model? A Case Study Using SWAT

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WATER RESOURCES RESEARCH 2022年第11期58卷 e2021WR031532-e2021WR031532页

作者： Qin, Youwei Kavetski, Dmitri Kuczera, George McInerney, David Yang, Tao Guo, Yuan Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing Peoples R China Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Univ Newcastle Sch Engn Callaghan NSW Australia

The calibration of highly parameterized hydrological models is a major computational challenge, especially for models with long run times. This challenge motivates the reconsideration of gradient-based algorithms often overlooked for their perceived lack of robustness. Our study evaluates two Gauss-Newton algorithms, robust Gauss-Newton (RGN), and Levenberg-Marquardt (PEST), and two stochastic algorithms, Shuffled Complex Evolution (SCE), and Dynamically Dimensioned Search (DDS), on a 38-parameter SWAT model calibration problem. algorithm performance is comprehensively assessed using trajectory plots from 100 invocations and by analyzing the distribution of estimated optima at fixed budgets of 200, 500, 1,000, 2,000, 3,000, and 5,000 objective function evaluations (model runs). Empirical results indicate that: (a) Gauss-Newton algorithms are more likely than stochastic algorithms to locate good solutions for the budgets considered in this work, and more likely to locate satisfactory solutions when budget is tight (200-500 model runs) and (b) RGN shows the fastest initial convergence amongst the algorithms under consideration and has the highest chance of finding satisfactory solutions when the budget is tight. The results indicate that Gauss-Newton algorithms offer an attractive choice for the calibration of highly parameterized hydrological models.

关键词： calibration of highly parameterized models fixed budget Gauss-Newton algorithm stochastic algorithm SWAT model

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Approximate replication of high-breakdown robust regression techniques

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Journal of Economic and Social Measurement 2009年第2-3期34卷 191-203页

作者： Zeileis, Achim Kleiber, Christian Department of Statistics and Mathematics WU Wirtschaftsuniversität Wien 1090 Wien Augasse 2-6 Austria Wirtschaftswissenschaftliches Zentrum (WWZ) Universität Basel 4002 Basel Peter Merian-Weg 6 Switzerland

We present a case study demonstrating that without data and code archives reproducibility is more the exception than the rule, especially if modern, complex algorithms are employed. Specifically, we show that stochastic extensions of OLS, as required in some combinatorial optimization problems arising in high-breakdown robust regression, can be difficult to replicate in the absence of detailed information on tuning parameters and further computational issues. © 2009-IOS Press and the authors. All rights reserved.

关键词： Combinatorial optimization Least squares Replication Robust regression stochastic algorithm

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A PARALLEL BLOCK-COORDINATE APPROACH FOR PRIMAL-DUAL SPLITTING WITH ARBITRARY RANDOM BLOCK SELECTION 23

A PARALLEL BLOCK-COORDINATE APPROACH FOR PRIMAL-DUAL SPLITTI...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Repetti, Audrey Chouzenoux, Emilie Pesquet, Jean-Christophe Univ Paris Est Lab Informat Gaspard Monge UMR CNRS 8049 Champs Sur Marne F-77454 Marne La Vallee France

ISBN: (纸本)9780992862633

The solution of many applied problems relies on finding the minimizer of a sum of smooth and/or nonsmooth convex functions possibly involving linear operators. In the last years, primal-dual methods have shown their efficiency to solve such minimization problems, their main advantage being their ability to deal with linear operators with no need to invert them. However, when the problem size becomes increasingly large, the implementation of these algorithms can be complicated, due to memory limitation issues. A simple way to overcome this difficulty consists of splitting the original numerous variables into blocks of smaller dimension, corresponding to the available memory, and to process them separately. In this paper we propose a random block-coordinate primal-dual algorithm, converging almost surely to a solution to the considered minimization problem. Moreover, an application to large-size 3D mesh denoising is provided to show the numerical efficiency of our method.

关键词： convex optimization nonsmooth optimization primal-dual algorithm stochastic algorithm parallel algorithm random block-coordinate approach proximity operator mesh denoising

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Efficient CUR Matrix Decomposition via Relative-Error Double-Sided Least Squares Solving 32

Efficient CUR Matrix Decomposition via Relative-Error Double...

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32nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Luan, Qi Zhao, Liang CUNY Grad Ctr PhD Program Math New York NY 10010 USA CUNY Lehman Coll Dept Comp Sci New York NY USA

ISBN: (纸本)9781728192284

Matrix CUR decomposition aims at representing a large matrix A with the product C . U . R, where C (resp. R) consists of a small collection of the original columns (resp. rows), and U is a small intermediate matrix connecting C and R. While modern randomized CUR algorithms have provided many efficient methods of choosing representative columns and rows, there hasn't been a method to find the optimal U matrix. In this paper, we present a sublinear-time randomized method to find good choices of the U matrix. Our proposed algorithm treats the task of finding U as a double-sided least squares problem min(Z) parallel to A - CZR parallel to(F), and is able to guarantee a close-to-optimal solution by solving a down-sampled problem of much smaller size. We provide worst-case analysis on its approximation error relative to theoretical optimal low-rank approximation error, and we demonstrate empirically how this method can improve the approximation of several large-scale real data matrices with a small number of additional computations.

关键词： Matrix Decomposition Sublinear algorithm stochastic algorithm CUR Decomposition

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Simulation Modeling of Glutamate Cysteine Ligase Activity

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Biophysics (Russian Federation) 2023年第2期68卷 157-166页

作者： Kopylova, V.S. Boronovskiy, S.E. Nartsissov, Ya. R. Institute of Cytochemistry and Molecular Pharmacology Moscow 115404 Russian Federation Biomedical Research Group BiDiPharma GmbH Siek 22962 Germany

Abstract: Glutathione (γ-glutamyl-cysteinyl-glycine) is one of the main intracellular antioxidants that play an important role in cellular metabolism. In mammalian cells, glutathione is synthesized in two stages, the first of which is catalyzed by glutamate–cysteine ligase and is limiting. In this study, a stochastic algorithm based on Markov chains with continuous time was used to simulate glutamate-cysteine ligase functioning. Several catalytic mechanisms were considered, taking the reverse inhibition of glutathione into account, as well as ATP binding order. Based on the physiological concentrations of the metabolites involved in the reaction, the rate of work of glutamate–cysteine ligase of human erythrocytes was calculated. Among the possible options for the substrate binding to the active site of the enzyme under study, only the mechanism involving primary ATP binding allows obtaining a value for the reaction rate corresponding to the experimentally measured glutamate-cysteine ligase activity at physiological substrate levels. In the case of other substrate binding schemes, the difference in rates was more than an order of magnitude. The analysis performed allows to conclude that when modeling glutathione biosynthesis under in vivo conditions, it is necessary to take into account both the ATP concentration and the reverse inhibition by glutathione. © 2023, Pleiades Publishing, Inc.

关键词： glutamate–cysteine ligase glutathione simulation modeling stochastic algorithm

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Pattern Discovery from High-Order Drug-Drug Interaction Relations

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Journal of Healthcare Informatics Research 2018年第3期2卷 272-304页

作者： Chiang, Wen-Hao Schleyer, Titus Shen, Li Li, Lang Ning, Xia Department of Computer & Information Science Indiana University - Purdue University Indianapolis Indianapolis 46202 IN United States Center for Biomedical Informatics Regenstrief Institute Indianapolis 46202 IN United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Pennsylvania 19104 PA United States Department of Biomedical Informatics The Ohio State University Columbus 43210 OH United States Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis 46202 IN United States

Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd’s) and directional DDI relations (DDI-d’s), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its stochastic algorithm SD ²ID ²S to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Convolution Drug-drug interactions Drug-drug similarities Graph representation stochastic algorithm

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