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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations David Dalton , Alan Lazarus , Hao Gao , Dirk Husmeier 25(272 1 61, 2024. Abstract We introduce a framework for designing boundary constrained Gaussian process BCGP priors for exact enforcement of linear boundary conditions , and apply it to the machine learning of initial boundary value problems involving linear partial differential equations PDEs In contrast to existing work , we illustrate how to design boundary constrained mean and kernel functions for all classes of
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us aeon : a Python Toolkit for Learning from Time Series Matthew Middlehurst , Ali Ismail-Fawaz , Antoine Guillaume , Christopher Holder , David Guijo-Rubio , Guzal Bulatova , Leonidas Tsaprounis , Lukasz Mentel , Martin Walter , Patrick Schäfer , Anthony Bagnall 25(289 1 10, 2024. Abstract aeon is a unified Python 3 library for all machine learning tasks involving time series . The package contains modules for time series forecasting , classification , extrinsic regression and clustering , as well as a variety of utilities , transformations and distance measures designed for time series data . aeon
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Geometric Learning with Positively Decomposable Kernels Nathael Da Costa , Cyrus Mostajeran , Juan-Pablo Ortega , Salem Said 25(326 1 42, 2024. Abstract Kernel methods are powerful tools in machine learning . Classical kernel methods are based on positive definite kernels , which enable learning in reproducing kernel Hilbert spaces RKHS For non-Euclidean data spaces , positive definite kernels are difficult to come by . In this case , we propose the use of reproducing kernel Krein space RKKS based methods , which require only kernels that admit a positive decomposition . We show that one does not
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Causal Discovery with Generalized Linear Models through Peeling Algorithms Minjie Wang , Xiaotong Shen , Wei Pan 25(310 1 49, 2024. Abstract This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes , including discrete , continuous , and mixed data . Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships . The proposed approach addresses this issue by developing two peeling algorithms bottom-up and top-down to ascertain causal relationships
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Commutative Scaling of Width and Depth in Deep Neural Networks Soufiane Hayou 25(299 1 41, 2024. Abstract In this paper , we study the commutativity of infinite width and depth limits in deep neural networks . Our aim is to understand the behavior of neural functions functions that depend on a neural network model as width and depth go to infinity in some sense and eventually identify settings under which commutativity holds , i.e . the neural function tends to the same limit no matter how width and depth limits are taken . In this paper , we formally introduce and define the commutativity framework
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Robust Principal Component Analysis using Density Power Divergence Subhrajyoty Roy , Ayanendranath Basu , Abhik Ghosh 25(324 1 40, 2024. Abstract Principal component analysis PCA is a widely employed statistical tool used primarily for dimensionality reduction . However , it is known to be adversely affected by the presence of outlying observations in the sample , which is quite common . Robust PCA methods using M-estimators have theoretical benefits , but their robustness drop substantially for high dimensional data . On the other end of the spectrum , robust PCA algorithms solving principal
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data Arhit Chakrabarti , Yang Ni , Ellen Ruth A . Morris , Michael L . Salinas , Robert S . Chapkin , Bani K . Mallick 25(323 1 56, 2024. Abstract We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph . To allow the sharing of clusters among the non-exchangeable groups , we propose a Bayesian nonparametric approach , termed graphical Dirichlet process , that jointly models the dependent group-specific random measures by
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Contamination-source based K-sample clustering Xavier Milhaud , Denys Pommeret , Yahia Salhi , Pierre Vandekerkhove 25(287 1 32, 2024. Abstract In this work , we investigate the K$-sample clustering of populations subject to contamination phenomena . A contamination model is a two-component mixture model where one component is known standard behaviour and the second component , modeling a departure from the standard behaviour , is unknown.When K$ populations from such a model are observed we propose a semiparametric clustering methodology to detect which populations are impacted by the same type of
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Value-Distributional Model-Based Reinforcement Learning Carlos E . Luis , Alessandro G . Bottero , Julia Vinogradska , Felix Berkenkamp , Jan Peters 25(298 1 42, 2024. Abstract Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks . We study the problem from a model-based Bayesian reinforcement learning perspective , where the goal is to learn the posterior distribution over value functions induced by parameter epistemic uncertainty of the Markov decision process . Previous work restricts the analysis to a few moments of the distribution
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Wasserstein Proximal Coordinate Gradient Algorithms Rentian Yao , Xiaohui Chen , Yun Yang 25(269 1 66, 2024. Abstract Motivated by approximation Bayesian computation using mean-field variational approximation and the computation of equilibrium in multi-species systems with cross-interaction , this paper investigates the composite geodesically convex optimization problem over multiple distributions . The objective functional under consideration is composed of a convex potential energy on a product of Wasserstein spaces and a sum of convex self-interaction and internal energies associated with each
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Optimistic Search : Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries Solt Kovács , Housen Li , Lorenz Haubner , Axel Munk , Peter Bühlmann 25(297 1 64, 2024. Abstract Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data . Searching one change point through all candidates requires O(n evaluations of the gain function for an interval with n$ observations . If each evaluation is computationally demanding e.g . in high-dimensional models this can become infeasible . Instead , we propose
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems Bokgyeong Kang , John Hughes , Murali Haran 25(286 1 32, 2024. Abstract Models with intractable normalizing functions have numerous applications . Because the normalizing constants are functions of the parameters of interest , standard Markov chain Monte Carlo cannot be used for Bayesian inference for these models . A number of algorithms have been developed for such models . Some have the posterior distribution as their asymptotic distribution . Other asymptotically inexact algorithms do not possess this property .
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The concentration of measure phenomenon serves an essential role in statistics and machine learning. This paper gives bounded difference-type concentration and moment inequalities for general functions of independent random variables with heavy tails. A general framework is presented, which can be used to prove inequalities for general functions once the moment inequality for sums of independent random variables is established. We illustrate the power of the framework by showing how it can be used to derive novel concentration and moment inequalities for bounded, Bernstein's moment condition, weak-exponential, and polynomial-moment random variables. Furthermore, we give potential applications of these inequalities to statistical learning theory.
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us MLRegTest : A Benchmark for the Machine Learning of Regular Languages Sam van der Poel , Dakotah Lambert , Kalina Kostyszyn , Tiantian Gao , Rahul Verma , Derek Andersen , Joanne Chau , Emily Peterson , Cody St . Clair , Paul Fodor , Chihiro Shibata , Jeffrey Heinz 25(283 1 45, 2024. Abstract Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification . This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest , which
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Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These implementations focus on modularity and easy extensibility to allow users to quickly modify/add to existing algorithms, or to implement new algorithms for different use cases. pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org.
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix Anindya Bhadra , Ksheera Sagar , David Rowe , Sayantan Banerjee , Jyotishka Datta 25(295 1 43, 2024. Abstract Marginal likelihood , also known as model evidence , is a fundamental quantity in Bayesian statistics . It is used for model selection using Bayes factors or for empirical Bayes tuning of prior hyper-parameters . Yet , the calculation of evidence has remained a longstanding open problem in Gaussian graphical models . Currently , the only feasible solutions that exist are for
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us A tensor factorization model of multilayer network interdependence Izabel Aguiar , Dane Taylor , Johan Ugander 25(282 1 54, 2024. Abstract Multilayer networks describe the rich ways in which nodes are related by accounting for different relationships in separate layers . These multiple relationships are naturally represented by an adjacency tensor . In this work we study the use of the nonnegative Tucker decomposition NNTuck of such tensors under a KL loss as an expressive factor model that naturally generalizes existing stochastic block models of multilayer networks . Quantifying interdependencies
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Empirical Design in Reinforcement Learning Andrew Patterson , Samuel Neumann , Martha White , Adam White 25(318 1 63, 2024. Abstract Empirical design in reinforcement learning is no small task . Running good experiments requires attention to detail and at times significant computational resources . While compute resources available per dollar have continued to grow rapidly , so have the scale of typical experiments in reinforcement learning . It is now common to benchmark agents with millions of parameters against dozens of tasks , each using the equivalent of 30 days of experience . The scale of
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Pure Differential Privacy for Functional Summaries with a Laplace-like Process Haotian Lin , Matthew Reimherr 25(305 1 50, 2024. Abstract Many existing mechanisms for achieving differential privacy DP on infinite-dimensional functional summaries typically involve embedding these functional summaries into finite-dimensional subspaces and applying traditional multivariate DP techniques . These mechanisms generally treat each dimension uniformly and struggle with complex , structured summaries . This work introduces a novel mechanism to achieve pure DP for functional summaries in a separable
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Sparse Recovery With Multiple Data Streams : An Adaptive Sequential Testing Approach Weinan Wang , Bowen Gang , Wenguang Sun 25(304 1 59, 2024. Abstract Multistage design has been utilized across a variety of scientific fields , enabling the adaptive allocation of sensing resources to effectively eliminate null locations and localize signals . We present a decision-theoretic framework for multi-stage adaptive testing that minimizes the total number of measurements while ensuring pre-specified constraints on both the false positive rate FPR and the missed discovery rate MDR Our method , SMART ,
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression Oliver Dukes , Stijn Vansteelandt , David Whitney 25(279 1 46, 2024. Abstract Due to concerns about parametric model misspecification , there is interest in using machine learning to adjust for confounding when evaluating the causal effect of an exposure on an outcome . Unfortunately , exposure effect estimators that rely on machine learning predictions are generally subject to so-called plug-in bias , which can render naive p-values and confidence intervals invalid . Progress has been made via proposals like targeted
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Inference on High-dimensional Single-index Models with Streaming Data Dongxiao Han , Jinhan Xie , Jin Liu , Liuquan Sun , Jian Huang , Bei Jiang , Linglong Kong 25(337 1 68, 2024. Abstract Traditional statistical methods are faced with new challenges due to streaming data . The major challenge is the rapidly growing volume and velocity of data , which makes storing such huge data sets in memory impossible . The paper presents an online inference framework for regression parameters in high-dimensional semiparametric single-index models with unknown link functions . The proposed online procedure
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us On Causality in Domain Adaptation and Semi-Supervised Learning : an Information-Theoretic Analysis for Parametric Models Xuetong Wu , Mingming Gong , Jonathan H . Manton , Uwe Aickelin , Jingge Zhu 25(261 1 57, 2024. Abstract Recent advancements in unsupervised domain adaptation UDA and semi-supervised learning SSL particularly incorporating causality , have led to significant methodological improvements in these learning problems . However , a formal theory that explains the role of causality in the generalization performance of UDA SSL is still lacking . In this paper , we consider the UDA SSL
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Learning and scoring Gaussian latent variable causal models with unknown additive interventions Armeen Taeb , Juan L . Gamella , Christina Heinze-Deml , Peter Bühlmann 25(293 1 68, 2024. Abstract With observational data alone , causal structure learning is a challenging problem . The task becomes easier when having access to data collected from perturbations of the underlying system , even when the nature of these is unknown . Existing methods either do not allow for the presence of latent variables or assume that these remain unperturbed . However , these assumptions are hard to justify if the
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning Luofeng Liao , Zuyue Fu , Zhuoran Yang , Yixin Wang , Dingli Ma , Mladen Kolar , Zhaoran Wang 25(303 1 56, 2024. Abstract In offline reinforcement learning RL an optimal policy is learned solely from a priori collected observational data . However , in observational data , actions are often confounded by unobserved variables . Instrumental variables IVs in the context of RL , are the variables whose influence on the state variables is all mediated by the action . When a valid instrument is present , we can recover the
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing Youran Qi , Xu He , Tzu-Hsiang Hung , Peter Chien 25(277 1 28, 2024. Abstract Compressed sensing is a signal processing technique used to efficiently acquire and reconstruct signals across various fields , including science , engineering , and business . A critical research challenge in compressed sensing is constructing a sensing matrix with desirable reconstruction properties . For optimal performance , the reconstruction process requires the sensing matrix to have low coherence . Several
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Identifying Causal Effects using Instrumental Time Series : Nuisance IV and Correcting for the Past Nikolaj Thams , Rikke Søndergaard , Sebastian Weichwald , Jonas Peters 25(302 1 51, 2024. Abstract Instrumental variable IV regression relies on instruments to infer causal effects from observational data with unobserved confounding . We consider IV regression in time series models , such as vector auto-regressive VAR processes . Direct applications of i.i.d . techniques are generally inconsistent as they do not correctly adjust for dependencies in the past . In this paper , we outline the difficulties
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Functional optimal transport : regularized map estimation and domain adaptation for functional data Jiacheng Zhu , Aritra Guha , Dat Do , Mengdi Xu , XuanLong Nguyen , Ding Zhao 25(276 1 49, 2024. Abstract We introduce a formulation of regularized optimal transport problem for distributions on function spaces , where the stochastic map between functional domains can be approximated in terms of an infinite-dimensional Hilbert-Schmidt operator mapping a Hilbert space of functions to another . For numerous machine learning applications , data can be naturally viewed as samples drawn from spaces of
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Studying the Interplay between Information Loss and Operation Loss in Representations for Classification Jorge F . Silva , Felipe Tobar , Mario Vicuña , Felipe Cordova 25(291 1 71, 2024. Abstract Information-theoretic measures have been widely adopted for machine learning ML feature design . Inspired by this , we look at the relationship between information loss in the Shannon sense and the operation loss in the minimum probability of error MPE sense when considering a family of lossy representations . Our first result offers a lower bound on a weak form of information loss as a function of its
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Stable and Consistent Density-Based Clustering via Multiparameter Persistence Alexander Rolle , Luis Scoccola 25(258 1 74, 2024. Abstract We consider the degree-Rips construction from topological data analysis , which provides a density-sensitive , multiparameter hierarchical clustering algorithm . We analyze its stability to perturbations of the input data using the correspondence-interleaving distance , a metric for hierarchical clusterings that we introduce . Taking certain one-parameter slices of degree-Rips recovers well-known methods for density-based clustering , but we show that these methods
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: Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Desiderata for Representation Learning : A Causal Perspective Yixin Wang , Michael I . Jordan 25(275 1 65, 2024. Abstract Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data . This learning problem is often approached by describing various desiderata associated with learned representations e.g . that they be non-spurious , efficient , or disentangled . It can be challenging , however , to turn these intuitive desiderata into formal criteria that can be measured and enhanced based on observed data . In this paper , we take a
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Faster Randomized Methods for Orthogonality Constrained Problems Boris Shustin , Haim Avron 25(257 1 59, 2024. Abstract Recent literature has advocated the use of randomized methods foraccelerating the solution of various matrix problems arising inmachine learning and data science . One popular strategy for leveraging randomization in numerical linear algebra is to use it as a way to reduce problem size . However , methods based on this strategy lack sufficient accuracy for some applications . Randomized preconditioning is another approach for leveraging randomization in numerical linear algebra ,
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: , , , Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us ENNS : Variable Selection , Regression , Classification , and Deep Neural Network for High-Dimensional Data Kaixu Yang , Arkaprabha Ganguli , Tapabrata Maiti 25(335 1 45, 2024. Abstract High-dimensional , low-sample-size HDLSS data have been attracting people's attention for a long time . Many studies have proposed different approaches to dealing with this situation , among which variable selection is a significant idea . However , neural networks have been used to model complicated relationships . This paper discusses current variable selection techniques with neural networks . We showed
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Home Page Papers Submissions News Editorial Board Special Issues Open Source Software Proceedings PMLR Data DMLR Transactions TMLR Search Statistics Login Frequently Asked Questions Contact Us Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure Meixia Lin , Defeng Sun , Kim-Chuan Toh , Chengjing Wang 25(256 1 36, 2024. Abstract Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph . The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities . We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure , which also allows additional linear
Updated: 2024-12-06 16:31:50
Sales : 1 888 845-1211 USA or 44 20 7193 9444 Europe customer login Toggle navigation Products AnyChart AnyStock AnyMap AnyGantt Mobile Qlik Extension Features Resources Business Solutions Technical Integrations Chartopedia Tutorials Support Company About Us Customers Success Stories More Testimonials News Download Buy Now Search News » Data Visualization Weekly » Recent Data Visualization Projects Worth Exploring â DataViz Weekly Recent Data Visualization Projects Worth Exploring â DataViz Weekly December 6th , 2024 by AnyChart Team Data visualization makes complex information accessible and insightful , serving as a valuable tool for both analysis and communication . This edition of DataViz Weekly features four recent projects that showcase its application across diverse . topics
Updated: 2024-12-05 19:30:00
I check out Claude, a generative AI assistant to see if, how, and when such a tool could fit into an analysis and visualization workflow.Tags: AI, Claude, generative
Updated: 2024-12-04 11:17:23
Add another graphic to the baby name genre of visualization. Karim Douïeb put…Tags: Karim Douïeb, names, text
Updated: 2024-12-04 02:03:55
New podcast episode details: David Spiegelhalter is Emeritus Professor of Statistics in the Statistical Laboratory, University of Cambridge and author of new book The Art of Uncertainty We live in chaotic times and David makes that world a little clearer with humour and clarity in this special interview with Alberto and Simon. The music this … Continue reading →
Updated: 2024-12-03 18:18:01
Sales : 1 888 845-1211 USA or 44 20 7193 9444 Europe customer login Toggle navigation Products AnyChart AnyStock AnyMap AnyGantt Mobile Qlik Extension Features Resources Business Solutions Technical Integrations Chartopedia Tutorials Support Company About Us Customers Success Stories More Testimonials News Download Buy Now Search News » Tips and Tricks » Integrating AnyChart JS Charts in Python Django Financial Trading Dashboard Integrating AnyChart JS Charts in Python Django Financial Trading Dashboard December 3rd , 2024 by Michael Whittle We are pleased to share an insightful guest article by Michael Whittle , a seasoned solution architect and developer with over 20 years of experience . Originally published on EODHD.com it explores how he integrated our JavaScript charting library
Updated: 2024-11-29 18:13:19
The post Great Examples of Real-World Data Visualizations — DataViz Weekly appeared first on AnyChart News.
Updated: 2024-11-21 00:37:50
Legend is a vital element in many charts, helping viewers quickly understand what each visual component represents. However, it is not always a must-have for every chart type. For instance, Gantt charts often work perfectly fine without a legend, so it is not enabled in our JavaScript Gantt Chart by default. That said, creating one is […]
The post Gantt Chart Legend — JS Chart Tips appeared first on AnyChart News.
Updated: 2024-11-15 22:52:38
Welcome back to DataViz Weekly, where we spotlight the most awesome data visualization works we have recently come across. Check out the projects we’re diving into this time: Love songs: death or evolution? — The Pudding Historical dry streaks in NYC — Bloomberg Green NYC subway ridership in detail — Subway Stories Vote swings in […]
The post Awesome New Data Visualization Works — DataViz Weekly appeared first on AnyChart News.
