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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Bounding the sum of the largest signless Laplacian eigenvalues of a graph

arXiv

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arXiv 2022年

作者： Abiad, Aida de Lima, Leonardo Kalantarzadeh, Sina Mohammadi, Mona Oliveira, Carla Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium Graduate Program in Mathematics Federal University of Parana Curitiba Brazil Sharif University of Technology Iran Department of Mathematical National School of Statistical Sciences Rio de Janeiro Brazil

关键词： Eigenvalues and eigenfunctions

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Improving structure evaluation through automatic hierarchy expansion 20

Improving structure evaluation through automatic hierarchy e...

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20th International Society for Music Information Retrieval Conference, ISMIR 2019

作者： McFee, Brian Kinnaird, Katherine M. Music and Audio Research Lab Center for Data Science New York University United States Department of Computer Science Statistical and Data Sciences Program Smith College United States

ISBN: (纸本)9781732729919

Structural segmentation is the task of partitioning a recording into non-overlapping time intervals, and labeling each segment with an identifying marker such as A, B, or verse. Hierarchical structure annotation expands this idea to allow an annotator to segment a song with multiple levels of granularity. While there has been recent progress in developing evaluation criteria for comparing two hierarchical annotations of the same recording, the existing methods have known deficiencies when dealing with inexact label matchings and sequential label repetition. In this article, we investigate methods for automatically enhancing structural annotations by inferring (and expanding) hierarchical information from the segment labels. The proposed method complements existing techniques for comparing hierarchical structural annotations by coarsening or refining labels with variation markers to either collapse similarly labeled segments together, or separate identically labeled segments from each other. Using the multi-level structure annotations provided in the SALAMI dataset, we demonstrate that automatic hierarchy expansion allows structure comparison methods to more accurately assess similarity between annotations. © 2020 International Society for Music Information Retrieval. All rights reserved.

关键词： Coarsening

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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A general semi-parametric elliptical distribution model for semi-supervised learning

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Journal of Nonparametric Statistics 2025年第2期37卷

作者： Chin-Tsang Chiang Sheng-Hsin Fan Ming-Yueh Huang Jen-Chieh Teng Alvin Lim a Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan b Department of Mathematics National Taiwan University Taipei Taiwan c Institute of Statistical Science Academia Sinica Taipei Taiwan d Data Science Degree Program National Taiwan University Taipei Taiwan e Data Science Department Measured Inc. Austin TX USAf Goizueta Business School Emory University Atlanta GA USA

ABSTRACTThis research proposes a novel semi-parametric elliptical distribution model for application in semi-supervised learning tasks. We use labelled and unlabelled data to develop a pseudo maximum likelihood method for estimation and classification. The proposed estimator outperforms the estimator based solely on labelled data and achieves the semi-parametric efficiency bound with a suitable size of unlabelled data. We efficiently maximise the objective function by utilising low-dimensional groupwise pseudo-likelihood functions in a block coordinate descent manner while ensuring numerical stability and convergence through appropriate bandwidth selectors and initial parameter estimates. Additionally, the study comprehensively investigates the impact of labelled and unlabelled data on the pseudo maximum likelihood estimator and classifier. Simulation studies and empirical data applications illustrate the superiority of our methodology.

关键词： elliptical distribution posterior group probability pseudo maximum likelihood method semi-parametric efficiency semi-supervised learning

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SCALE RELIANT INFERENCE

arXiv

引用

arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department

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Computer Methods and programs in Biomedicine 2024年 255卷 108357页

作者： Lee, Chih-Kuo Chen, Ting-Li Wu, Jeng-En Liao, Min-Tsun Wang, Chiehhung Wang, Weichung Chou, Cheng-Ying Department of Internal Medicine National Taiwan University Hospital Hsin-Chu Branch No. 25 Ln. 442 Sec. 1 Jingguo Rd. Taiwan Hsinchu300 Taiwan Institute of Statistical Science Academia Sinica 128 Academia Rd. Nankang Taiwan Taipei11529 Taiwan Master Program in Statistics National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Data Science Degree Program National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Institute of Applied Mathematical Sciences National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Department of Biomechatronics Engineering National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan

Background and Objectives: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments. Methods: Using the open-source dataset MIMIC-IV and MIMIC[sbnd]CXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs. We processed the CXRs, extracted relevant features through lung-heart masks, and combined these with the vital signs at triage to predict corresponding NT-proBNP levels. Results: The proposed method achieved a 0.89 area under the receiver operating characteristic curve by fusing predictions from single-modality models of heart size ratio, radiomic features, CXR, and the region of interest in the CXR. The model can accurately predict dyspneic patients with abnormal NT-proBNP concentrations, allowing physicians to reduce the risks associated with inappropriate treatment. Conclusion: The study provided new image features related to AHF and offered insights into future research directions. Overall, these models have great potential to improve patient outcomes and reduce risks in emergency departments. © 2024 Elsevier B.V.

关键词： Image segmentation

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Facilitating team-based data science: Lessons learned from the DSC-WAV project

arXiv

引用

arXiv 2021年

作者： Legacy, Chelsey Zieffler, Andrew Baumer, Benjamin S. Barr, Valerie Horton, Nicholas J. Department of Educational Psychology University of Minnesota MinneapolisMN55455 United States Program in Statistical & Data Sciences Smith College NorthamptonMA01063 United States Department of Computer Science Mount Holyoke College South HadleyMA01075 United States Department of Mathematics and Statistics Amherst College Amherst MA01002 United States

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students’ perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own *** Codes 97K80, 97P99 Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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A Consensus Privacy Metrics Framework for Synthetic data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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