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In this talk, we present a CNN architecture for predicting autoregressive asynchronous time series. We illustrate its application on predicting traders’ quotes of credit default swaps (proprietary dataset from Hellebore Capital), and on artificial time series. The paper is available there: http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf

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A review of two decades of correlations, hierarchies, networks and clustering...

Opinionated review of two decades of correlations, hierarchies,
networks and clustering in financial markets presented at Ton Duc Thang University in Ho Chi Minh City, Vietnam.

Network and risk spillovers: a multivariate GARCH perspective

M. Billio, M. Caporin, L. Frattarolo, L. Pelizzon: “Network and risk spillovers: a multivariate GARCH perspective”.
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016

Entropy and systemic risk measures

Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016

Clustering CDS: algorithms, distances, stability and convergence rates

Talk given at CMStatistics 2016 (http://cmstatistics.org/CMStatistics2016/).
The standard methodology for clustering financial time series is quite brittle to outliers / heavy-tails for many reasons: Single Linkage / MST suffers from the chaining phenomenon; Pearson correlation coefficient is relevant for Gaussian distributions which is usually not the case for financial returns (especially for credit derivatives). At Hellebore Capital Ltd, we strive to improve the methodology and to ground it. We think that stability is a paramount property to verify, which is closely linked to statistical convergence rates of the methodologies (combination of clustering algorithms and dependence estimators). This gives us a model selection criterion: The best clustering methodology is the methodology that can reach a given 'accuracy' with the minimum sample size.

A short introduction to statistical learning

This document provides an introduction to statistical learning methods. It begins with background information on statistical learning problems and discusses concepts like underfitting, overfitting, and consistency. It then summarizes decision trees and random forests, describing how they are learned from data and make predictions. Support vector machines and neural networks are also briefly mentioned. Key goals of statistical learning methods include accuracy on training data as well as generalization to new data.

Clustering in dynamic causal networks as a measure of systemic risk on the eu...

Clustering in dynamic causal networks as a measure of systemic risk on the euro zone
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IESEG/ Universitè Paris1 Panthèon-Sorbonne/ University Ca' Foscari
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016

Exploring Quantum Supremacy in Access Structures of Secret Sharing by Coding ...

Quantum supremacy or quantum advantage is the potential ability of quantum computing devices to solve problems that classical computers practically cannot (Wikipedia). The speaker recently found that quantum computation can realize secret sharing schemes that cannot be realized by any classical computation. That finding was enabled by combinatorial studies of quantum error-correcting codes and classical secret sharing. This talk introduces those studies to non-specialists with mathematical backgrounds.
Reference: arXiv:1803.10392

Options on Quantum Money: Quantum Path- Integral With Serial Shocks

The author previously developed a numerical multivariate path-integral algorithm, PATHINT, which has been applied to several classical physics systems, including statistical mechanics of neocortical interactions, options in financial markets, and other nonlinear systems including chaotic systems. A new quantum version, qPATHINT, has the ability to take into account nonlinear and time-dependent modifications of an evolving system. qPATHINT is shown to be useful to study some aspects of serial changes to systems. Applications to options on quantum money and blockchains in financial markets are discussed.

A review of two decades of correlations, hierarchies, networks and clustering...

Opinionated review of two decades of correlations, hierarchies,
networks and clustering in financial markets presented at Ton Duc Thang University in Ho Chi Minh City, Vietnam.

Network and risk spillovers: a multivariate GARCH perspective

M. Billio, M. Caporin, L. Frattarolo, L. Pelizzon: “Network and risk spillovers: a multivariate GARCH perspective”.
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016

Entropy and systemic risk measures

Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016

Clustering CDS: algorithms, distances, stability and convergence rates

Talk given at CMStatistics 2016 (http://cmstatistics.org/CMStatistics2016/).
The standard methodology for clustering financial time series is quite brittle to outliers / heavy-tails for many reasons: Single Linkage / MST suffers from the chaining phenomenon; Pearson correlation coefficient is relevant for Gaussian distributions which is usually not the case for financial returns (especially for credit derivatives). At Hellebore Capital Ltd, we strive to improve the methodology and to ground it. We think that stability is a paramount property to verify, which is closely linked to statistical convergence rates of the methodologies (combination of clustering algorithms and dependence estimators). This gives us a model selection criterion: The best clustering methodology is the methodology that can reach a given 'accuracy' with the minimum sample size.

A short introduction to statistical learning

This document provides an introduction to statistical learning methods. It begins with background information on statistical learning problems and discusses concepts like underfitting, overfitting, and consistency. It then summarizes decision trees and random forests, describing how they are learned from data and make predictions. Support vector machines and neural networks are also briefly mentioned. Key goals of statistical learning methods include accuracy on training data as well as generalization to new data.

Clustering in dynamic causal networks as a measure of systemic risk on the eu...

Clustering in dynamic causal networks as a measure of systemic risk on the euro zone
M. Billio, H. Gatfaoui, L. Frattarolo, P. de Peretti
IESEG/ Universitè Paris1 Panthèon-Sorbonne/ University Ca' Foscari
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016

Exploring Quantum Supremacy in Access Structures of Secret Sharing by Coding ...

Quantum supremacy or quantum advantage is the potential ability of quantum computing devices to solve problems that classical computers practically cannot (Wikipedia). The speaker recently found that quantum computation can realize secret sharing schemes that cannot be realized by any classical computation. That finding was enabled by combinatorial studies of quantum error-correcting codes and classical secret sharing. This talk introduces those studies to non-specialists with mathematical backgrounds.
Reference: arXiv:1803.10392

Options on Quantum Money: Quantum Path- Integral With Serial Shocks

The author previously developed a numerical multivariate path-integral algorithm, PATHINT, which has been applied to several classical physics systems, including statistical mechanics of neocortical interactions, options in financial markets, and other nonlinear systems including chaotic systems. A new quantum version, qPATHINT, has the ability to take into account nonlinear and time-dependent modifications of an evolving system. qPATHINT is shown to be useful to study some aspects of serial changes to systems. Applications to options on quantum money and blockchains in financial markets are discussed.

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You may have already read many times that the job of a Data Scientist is to skim through a huge amount of data searching for correlations between some variables of interest. And also, that one of his worst enemies (besides correlation doesn't imply causation) is spurious correlation. But what really is correlation? Are there several types of correlations? Some "good", some "bad"? What about their estimation? This talk will be a very visual presentation around the notion of correlation and dependence. I will first illustrate how the standard linear correlation is estimated (Pearson coefficient), then some more robust alternative: the Spearman coefficient. Building on the geometric understanding of their nature, I will present a generalization that can help Data Scientists to explore, interpret, and measure the dependence (not necessarily linear or comonotonic) between the variables of a given dataset. Financial time series (stocks, credit default swaps, fx rates), and features from the UCI datasets are considered as use cases.

Clustering Financial Time Series: How Long is Enough?

IJCAI-16, New York, conference presentation of paper http://www.ijcai.org/Proceedings/16/Papers/367.pdf
Researchers have used from 30 days to several
years of daily returns as source data for clustering
financial time series based on their correlations.
This paper sets up a statistical framework to study
the validity of such practices. We first show that
clustering correlated random variables from their
observed values is statistically consistent. Then,
we also give a first empirical answer to the much
debated question: How long should the time series
be? If too short, the clusters found can be spurious;
if too long, dynamics can be smoothed out.

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Quaternions provide a mathematical notation for representing object orientations and rotations in 3D. They are simpler to compose than Euler angles and avoid issues like gimbal lock. Quaternions also have better numerical stability than rotation matrices and may be more computationally efficient. A quaternion is represented as a + bi + cj + dk, and can be converted to and from a rotation matrix. The trace of a matrix is the sum of its main diagonal, and is used to choose the diagonal element of largest absolute value for a robust conversion method between quaternions and matrices.

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This document presents a study comparing a standard route choice model (SRUM) to prospect theory models of route choice (CPT models) with context-dependent reference points. The CPT models better fit the experimental route choice data than the SRUM, particularly models using relative reference points based on average time outcomes. Estimation of the CPT models confirmed loss aversion for both travel time attributes. The study provides empirical support for using prospect theory to model travelers' risk attitudes in time-related route choice decisions.

Forecasting Gasonline Price in Vietnam Based on Fuzzy Time Series and Automat...

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This document introduces machine learning strategies for time series prediction. It begins with an introduction to the speaker and his background and research interests. It then provides an outline of the topics to be covered, including notions of time series, machine learning approaches for prediction, local learning methods, forecasting techniques, and applications and future directions. The document discusses what the audience should know coming into the course and what they will learn.

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This document provides an overview of computational intelligence methods for time series prediction. It begins with introductions to time series analysis and machine learning approaches for prediction. Specific models discussed include autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. Parameter estimation techniques for AR models are also covered. The document outlines applications in areas like forecasting, wireless sensors, and biomedicine and concludes with perspectives on future directions.

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Application of transportation problem under pentagonal neutrosophic environment

Application of transportation problem under pentagonal neutrosophic environmentJournal of Fuzzy Extension and Applications

The paper talks about the pentagonal Neutrosophic sets and its operational law. The paper presents the cut of single valued pentagonal Neutrosophic numbers and additionally introduced the arithmetic operation of single-valued pentagonal Neutrosophic numbers. Here, we consider a transportation problem with pentagonal Neutrosophic numbers where the supply, demand and transportation cost is uncertain. Taking the benefits of the properties of ranking functions, our model can be changed into a relating deterministic form, which can be illuminated by any method. Our strategy is easy to assess the issue and can rank different sort of pentagonal Neutrosophic numbers. To legitimize the proposed technique, some numerical tests are given to show the adequacy of the new model.Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions

This document describes a study that uses a large dataset of vehicle travel times in the Greater Tokyo Area to build nonparametric models of travel time distributions on each road link. The authors propose a conditional density estimator (CDE) that models each link's travel time distribution as a weighted mixture of basis density functions determined from neighboring links. The CDE is fitted to the data in three steps: 1) determining the basis density functions via convex clustering, 2) defining link similarities based on a sparse diffusion kernel on a link connectivity graph, and 3) optimizing link importance weights. Experimental results show the CDE approach outperforms parametric regression baselines in predictive performance, demonstrating its ability to model complex, non-Gaussian travel time

A new-quantile-based-fuzzy-time-series-forecasting-model

The document presents a new quantile based fuzzy time series forecasting model. It begins by reviewing existing fuzzy time series forecasting methods and their applications. It then proposes a new method that bases forecasts on predicting future trends in the data using third order fuzzy relationships. The method converts statistical quantiles into fuzzy quantiles using membership functions. It uses a fuzzy metric and trend forecast to calculate future values. The method is applied to TAIFEX index forecasting. Results show the proposed method performs comparably better than other fuzzy time series methods in terms of complexity and forecasting accuracy.

Varese italie seminar

This document summarizes a seminar on econometrics and machine learning given by Arthur Charpentier at Università degli studi dell’Insubria in May 2018. It discusses the history and development of econometrics, including its probabilistic foundations. It also covers key econometric techniques like regression, maximum likelihood estimation, and nonparametric methods. Model selection criteria like AIC and BIC are also briefly discussed. The document provides a high-level overview of major topics in econometrics through the lens of its use in large datasets and connection to machine learning.

Slides ub-1

This document contains an agenda and introduction for a summer school lecture series on big data for economics given by Arthur Charpentier. The introduction discusses how big data brings new questions to economics due to issues like the curse of dimensionality and new types of data like text, network, and tensor data. It provides examples of how these new types of data differ from traditional individual data and require new techniques. The document also introduces two toy datasets that will be used in the lectures: a medical dataset on myocardial infarction and a simulated binary choice dataset.

Cari2020 Parallel Hybridization for SAT: An Efficient Combination of Search S...

This document summarizes a research paper on parallel hybridization techniques for solving Boolean satisfiability problems. It describes combining search space splitting and portfolio approaches to leverage their strengths while avoiding weaknesses. Experimental results on hard SAT instances showed the proposed approach solved more problems faster than a baseline portfolio solver. Future work could investigate optimizing parameters like the "jump factor" and addressing unsatisfiable instances.

Cari 2020: A minimalistic model of spatial structuration of humid savanna veg...

The document describes a minimalistic spatial model of vegetation structuring in humid savanna environments. It aims to illustrate the emergence of spatial patterns and highlight fire resistance strategies of trees. The model builds on previous work, incorporating a spatial component to represent how tree and grass biomass change over space and time based on logistic growth, nonlocal competition/cooperation, and fire mortality probabilities dependent on tree density. Mathematical analysis and numerical illustration of the model will provide insights into savanna vegetation structuring.

A closer look at correlations

You may have already read many times that the job of a Data Scientist is to skim through a huge amount of data searching for correlations between some variables of interest. And also, that one of his worst enemies (besides correlation doesn't imply causation) is spurious correlation. But what really is correlation? Are there several types of correlations? Some "good", some "bad"? What about their estimation? This talk will be a very visual presentation around the notion of correlation and dependence. I will first illustrate how the standard linear correlation is estimated (Pearson coefficient), then some more robust alternative: the Spearman coefficient. Building on the geometric understanding of their nature, I will present a generalization that can help Data Scientists to explore, interpret, and measure the dependence (not necessarily linear or comonotonic) between the variables of a given dataset. Financial time series (stocks, credit default swaps, fx rates), and features from the UCI datasets are considered as use cases.

Clustering Financial Time Series: How Long is Enough?

IJCAI-16, New York, conference presentation of paper http://www.ijcai.org/Proceedings/16/Papers/367.pdf
Researchers have used from 30 days to several
years of daily returns as source data for clustering
financial time series based on their correlations.
This paper sets up a statistical framework to study
the validity of such practices. We first show that
clustering correlated random variables from their
observed values is statistically consistent. Then,
we also give a first empirical answer to the much
debated question: How long should the time series
be? If too short, the clusters found can be spurious;
if too long, dynamics can be smoothed out.

Quaternion to Matrix, Matrix to Quaternion

Quaternions provide a mathematical notation for representing object orientations and rotations in 3D. They are simpler to compose than Euler angles and avoid issues like gimbal lock. Quaternions also have better numerical stability than rotation matrices and may be more computationally efficient. A quaternion is represented as a + bi + cj + dk, and can be converted to and from a rotation matrix. The trace of a matrix is the sum of its main diagonal, and is used to choose the diagonal element of largest absolute value for a robust conversion method between quaternions and matrices.

cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Dist...

A Generative Adversarial Networks model to generate realistic correlation matrices. In these slides, we discuss a use case in quantitative finance (comparison of risk-based portfolio allocation methods), and how to improve the seminal model with information geometry (Riemannian neural networks suited for correlation matrices). There are many use cases to explore within, and outside, quantitative finance. The Riemannian geometry of correlation matrices is still under-developed.
We highlight exciting problems at the intersection of Riemannian geometry and deep learning.

A Logical Language with a Prototypical Semantics

ABSTRACT: In a pair of papers from 1995 and 1997, I developed a computational theory of legal argument, but left open a question about the key concept of a "prototype." Contemporary trends in machine learning have now shed new light on the subject. In this talk, I will describe my recent work on "manifold learning," as well as some work in progress on "deep learning." Taken together, this work leads to a logical language grounded in a prototypical perceptual semantics, with implications for legal theory.

Extracting biclusters of similar values with Triadic Concept Analysis

Extracting biclusters of similar values with Triadic Concept AnalysisINSA Lyon - L'Institut National des Sciences Appliquées de Lyon

TriMax is an algorithm that uses formal concept analysis (FCA) and triadic concept analysis to extract maximal biclusters of similar values from numerical data. It builds a triadic context from the data using tolerance blocks of values and then applies FCA algorithms to extract triadic concepts, which correspond to maximal biclusters. Experiments on gene expression data show that TriMax scales to large datasets and outputs biclusters faster than existing algorithms, while providing a complete and non-redundant set of patterns. The algorithm can also incorporate additional constraints to filter bicluster candidates.Pittsburgh and Toronto "Halloween US trip" seminars

ABC stands for approximate Bayesian computation. ABC methods are used when the likelihood function is intractable or impossible to compute directly. ABC produces approximate samples from the posterior distribution by simulating data under different parameter values and comparing them to the observed data. ABC has been widely applied in population genetics where genealogies make likelihoods difficult to calculate. While ABC provides a practical computational approach, it raises questions about how closely it relates to true Bayesian inference with the raw data.

Blind Signal Separation & Multivariate Non-Linear Dependency Measures - Peter...

Blind Signal Separation & Multivariate Non-Linear Dependency Measures - Peter Addo. December, 15 2014. CSRA research meeting

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Marcus Gallagher gave a talk on explicit modelling in metaheuristic optimization. He discussed estimation of distribution algorithms which use probabilistic models to represent promising regions of the search space. He provided examples of modelling approaches like PBIL, MIMIC, COMIT and BOA. Finally, he summarized that EDAs take an explicit modelling approach to optimization using existing statistical models and can solve challenging problems by visualizing the model.

A Monte Carlo strategy for structure multiple-step-head time series prediction

The document proposes a Monte Carlo approach called SMC (Structured Monte Carlo) for multiple-step-ahead time series forecasting that takes into account the structural dependencies between predictions. It generates samples using a direct forecasting approach and weights them based on how well they satisfy dependencies identified by an iterated approach. Experiments on three benchmark datasets show the SMC approach achieves more accurate forecasts as measured by SMAPE than iterated, direct, or other comparison methods for most prediction horizons tested.

A prospect theory model of route choice with context dependent reference points

This document presents a study comparing a standard route choice model (SRUM) to prospect theory models of route choice (CPT models) with context-dependent reference points. The CPT models better fit the experimental route choice data than the SRUM, particularly models using relative reference points based on average time outcomes. Estimation of the CPT models confirmed loss aversion for both travel time attributes. The study provides empirical support for using prospect theory to model travelers' risk attitudes in time-related route choice decisions.

Forecasting Gasonline Price in Vietnam Based on Fuzzy Time Series and Automat...

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This document presents a new forecasting model that combines fuzzy time series and automatic clustering techniques to forecast gasoline prices in Vietnam. The model first uses an automatic clustering algorithm to divide historical gasoline price data into clusters with varying interval lengths. It then fuzzifies the data based on the new intervals to determine fuzzy logical relationships and forecasted values. The model is applied to a dataset of gasoline prices in Vietnam. Results show the proposed model achieves higher forecasting accuracy than a first-order fuzzy time series model.Machine Learning Strategies for Time Series Prediction

This document introduces machine learning strategies for time series prediction. It begins with an introduction to the speaker and his background and research interests. It then provides an outline of the topics to be covered, including notions of time series, machine learning approaches for prediction, local learning methods, forecasting techniques, and applications and future directions. The document discusses what the audience should know coming into the course and what they will learn.

Computational Intelligence for Time Series Prediction

This document provides an overview of computational intelligence methods for time series prediction. It begins with introductions to time series analysis and machine learning approaches for prediction. Specific models discussed include autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. Parameter estimation techniques for AR models are also covered. The document outlines applications in areas like forecasting, wireless sensors, and biomedicine and concludes with perspectives on future directions.

Unit 6: All

The document discusses limitations of Shannon entropy and computable measures. It describes an algorithm for creating a simple graph by iteratively adding nodes to maximize the number of "core nodes". The degree sequence of labeled nodes forms the Champernowne constant in base 10. The sequence of number of edges is a function of core and supportive nodes. Maximum entropy and algorithmic complexity principles are discussed. The Coding Theorem Method is introduced for computing the uncomputable. Computability mediates challenges in causality via algorithmic information theory. The Block Decomposition Method joins Shannon entropy and algorithmic complexity by allowing a tighter upper bound on complexity than analyzing parts separately. Examples are given of applying these methods.

Application of transportation problem under pentagonal neutrosophic environment

Application of transportation problem under pentagonal neutrosophic environmentJournal of Fuzzy Extension and Applications

The paper talks about the pentagonal Neutrosophic sets and its operational law. The paper presents the cut of single valued pentagonal Neutrosophic numbers and additionally introduced the arithmetic operation of single-valued pentagonal Neutrosophic numbers. Here, we consider a transportation problem with pentagonal Neutrosophic numbers where the supply, demand and transportation cost is uncertain. Taking the benefits of the properties of ranking functions, our model can be changed into a relating deterministic form, which can be illuminated by any method. Our strategy is easy to assess the issue and can rank different sort of pentagonal Neutrosophic numbers. To legitimize the proposed technique, some numerical tests are given to show the adequacy of the new model.Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions

This document describes a study that uses a large dataset of vehicle travel times in the Greater Tokyo Area to build nonparametric models of travel time distributions on each road link. The authors propose a conditional density estimator (CDE) that models each link's travel time distribution as a weighted mixture of basis density functions determined from neighboring links. The CDE is fitted to the data in three steps: 1) determining the basis density functions via convex clustering, 2) defining link similarities based on a sparse diffusion kernel on a link connectivity graph, and 3) optimizing link importance weights. Experimental results show the CDE approach outperforms parametric regression baselines in predictive performance, demonstrating its ability to model complex, non-Gaussian travel time

A new-quantile-based-fuzzy-time-series-forecasting-model

The document presents a new quantile based fuzzy time series forecasting model. It begins by reviewing existing fuzzy time series forecasting methods and their applications. It then proposes a new method that bases forecasts on predicting future trends in the data using third order fuzzy relationships. The method converts statistical quantiles into fuzzy quantiles using membership functions. It uses a fuzzy metric and trend forecast to calculate future values. The method is applied to TAIFEX index forecasting. Results show the proposed method performs comparably better than other fuzzy time series methods in terms of complexity and forecasting accuracy.

Cari2020 Parallel Hybridization for SAT: An Efficient Combination of Search S...

Cari2020 Parallel Hybridization for SAT: An Efficient Combination of Search S...

Cari 2020: A minimalistic model of spatial structuration of humid savanna veg...

Cari 2020: A minimalistic model of spatial structuration of humid savanna veg...

A closer look at correlations

A closer look at correlations

Clustering Financial Time Series: How Long is Enough?

Clustering Financial Time Series: How Long is Enough?

Quaternion to Matrix, Matrix to Quaternion

Quaternion to Matrix, Matrix to Quaternion

cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Dist...

cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Dist...

A Logical Language with a Prototypical Semantics

A Logical Language with a Prototypical Semantics

Extracting biclusters of similar values with Triadic Concept Analysis

Extracting biclusters of similar values with Triadic Concept Analysis

Pittsburgh and Toronto "Halloween US trip" seminars

Pittsburgh and Toronto "Halloween US trip" seminars

Blind Signal Separation & Multivariate Non-Linear Dependency Measures - Peter...

Blind Signal Separation & Multivariate Non-Linear Dependency Measures - Peter...

Learning for Optimization: EDAs, probabilistic modelling, or ...

Learning for Optimization: EDAs, probabilistic modelling, or ...

A Monte Carlo strategy for structure multiple-step-head time series prediction

A Monte Carlo strategy for structure multiple-step-head time series prediction

A prospect theory model of route choice with context dependent reference points

A prospect theory model of route choice with context dependent reference points

Forecasting Gasonline Price in Vietnam Based on Fuzzy Time Series and Automat...

Forecasting Gasonline Price in Vietnam Based on Fuzzy Time Series and Automat...

Machine Learning Strategies for Time Series Prediction

Machine Learning Strategies for Time Series Prediction

Computational Intelligence for Time Series Prediction

Computational Intelligence for Time Series Prediction

Unit 6: All

Unit 6: All

Application of transportation problem under pentagonal neutrosophic environment

Application of transportation problem under pentagonal neutrosophic environment

Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions

Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions

A new-quantile-based-fuzzy-time-series-forecasting-model

A new-quantile-based-fuzzy-time-series-forecasting-model

Varese italie seminar

This document summarizes a seminar on econometrics and machine learning given by Arthur Charpentier at Università degli studi dell’Insubria in May 2018. It discusses the history and development of econometrics, including its probabilistic foundations. It also covers key econometric techniques like regression, maximum likelihood estimation, and nonparametric methods. Model selection criteria like AIC and BIC are also briefly discussed. The document provides a high-level overview of major topics in econometrics through the lens of its use in large datasets and connection to machine learning.

Slides ub-1

This document contains an agenda and introduction for a summer school lecture series on big data for economics given by Arthur Charpentier. The introduction discusses how big data brings new questions to economics due to issues like the curse of dimensionality and new types of data like text, network, and tensor data. It provides examples of how these new types of data differ from traditional individual data and require new techniques. The document also introduces two toy datasets that will be used in the lectures: a medical dataset on myocardial infarction and a simulated binary choice dataset.

Probabilistic Modelling with Information Filtering Networks

Information filtering networks can be used to construct sparse probabilistic models for predictions and risk quantification

Multimodal Deep Learning

Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.

1.IntroDescriptiveDisplay-20222023WS.pdf

This example shows a correlation, but does not prove causation. While the Redskins' performance and election outcomes changed together frequently, the football game results did not cause the election results.
18/10/2022
Prof. Dr. Constantinos Antoniou | Applied Statistics in Transport 56
Correlation vs. Causation
Correlation:
- Two variables change together
- Does not imply one causes the other
Causation:
- One variable causes changes in the other
- Requires evidence the relationship is not due to chance or other factors
Just because two things correlate does NOT mean one causes the other. Additional analysis is needed to establish causation.
18/10/2022
Prof.

QMC: Transition Workshop - Selected Highlights from the Probabilistic Numeric...

QMC: Transition Workshop - Selected Highlights from the Probabilistic Numeric...The Statistical and Applied Mathematical Sciences Institute

This document summarizes the work of Working Group II on Probabilistic Numerics from the SAMSI QMC Transition Workshop. The working group aims to develop probabilistic numerical methods that provide a richer probabilistic quantification of numerical error in outputs, allowing for better statistical inference. Members of the working group have published several papers on topics like Bayesian probabilistic numerical methods for solving differential equations and performing integral approximations, and applying these methods to problems in mathematical epidemiology and industrial process monitoring. The group has also organized workshops and reading groups to discuss the development of probabilistic numerical methods.Selective and incremental re-computation in reaction to changes: an exercise ...

Invited research seminar given at Durham University, Computer Science about findings from the Recomp project http://recomp.org.uk/

“Un modelo basado en agentes para el estudio de la actividad en redes sociale...

“Un modelo basado en agentes para el estudio de la actividad en redes sociale...Complejidady Economía

Conferencia 13.agosto.2013 Seminario de Complejiad y Economía
“Un modelo basado en agentes para el estudio de la actividad en redes sociales online”Slides ub-2

1) The document discusses simulation-based techniques and the bootstrap method for economics.
2) It provides historical references for permutation methods dating back to Fisher (1935) and jackknife and bootstrapping which started with Monte Carlo algorithms in the 1940s.
3) Bootstrapping is introduced as an asymptotic refinement based on computer simulations that generates additional samples from the original data to reduce uncertainty, rather than collecting more observations.

QMC: Undergraduate Workshop, Monte Carlo Techniques in Earth Science - Amit A...

QMC: Undergraduate Workshop, Monte Carlo Techniques in Earth Science - Amit A...The Statistical and Applied Mathematical Sciences Institute

A crucial ingredient of a successful weather prediction system is its ability to combine observational data with the
output of numerical weather prediction models to estimate the state of the atmosphere and the oceans. This problem of estimation of the state of a high dimensional chaotic system such as the atmosphere, given noisy and partial observations of it is known as data assimilation in the context of earth sciences. The main object of interest in these problems is
the conditional distribution, called the posterior, of the state conditioned on the observations. Monte Carlo methods are the most commonly used techniques to study this posterior and also to use it efficiently for prediction. I will give a general introduction to the data assimilation problems and also to Monte Carlo techniques, followed by a discussion of some commonly used Monte Carlo algorithms for data assimilation.Kernel methods and variable selection for exploratory analysis and multi-omic...

Nathalie Vialaneix
4th course on Computational Systems Biology of Cancer: Multi-omics and Machine Learning Approaches
International course, Curie training
https://training.institut-curie.org/courses/sysbiocancer2021
(remote)
September 29th, 2021

2018 Modern Math Workshop - Foundations of Statistical Learning Theory: Quint...

2018 Modern Math Workshop - Foundations of Statistical Learning Theory: Quint...The Statistical and Applied Mathematical Sciences Institute

This document provides an introduction to statistical machine learning and statistical learning theory. It begins by acknowledging the invitation to present and then outlines topics to be covered, including input/output spaces, loss functions, risk functionals, generalization error, and regularization. Examples of applications like handwritten digit recognition and accent recognition are presented. It discusses challenges in classification problems like imbalanced data and complex decision boundaries. The goal of statistical learning theory is to minimize theoretical risk by finding the best predictive function, while accounting for limitations like an unknown data distribution.Inference via Bayesian Synthetic Likelihoods for a Mixed-Effects SDE Model of...

- The document describes a talk given by Umberto Picchini on Bayesian inference for a mixed-effects stochastic differential equation (SDE) model of tumor growth.
- The model introduces a state-space model for tumor growth in mice with dynamics driven by an SDE and formulates a mixed-effects SDE model to estimate population parameters.
- The talk aims to show how to perform approximate Bayesian inference for the mixed-effects SDE model using synthetic likelihoods.

Inventory theory presentation

This slide was used in the "Mathematics of Logistics" seminar at Nishinari Laboratory, Faculty of Engineering, the University of Tokyo.
references:
1.久保幹雄 (2007) 『ロジスティクスの数理』 共立出版
2.Dimitri P. Bertsekas (2005). Dynamic Programming and Optimal Control. Athena Scientific. Vol 1,2. 4th edition.

Kernel methods for data integration in systems biology

This document provides an overview of a seminar presentation on kernel methods for data integration in systems biology. It begins with short biographies of the presenter, who is trained as a mathematician and statistician and applies their skills to research in human health and animal genomics using various omics data types. Examples are given of the presenter's past work inferring networks and integrating gene expression and lipid data, as well as expression and 3D DNA location data. The talk will discuss how to integrate multiple omics data from different sources and types using kernels. Kernels allow reducing high-dimensional data to similarity matrices and are not restricted to numeric data. They also allow embedding expert knowledge and provide a framework for statistical learning.

Learning Intrusion Prevention Policies Through Optimal Stopping

The document discusses formulating intrusion prevention as an optimal stopping problem. It describes a use case where a defender monitors an infrastructure for signs of intrusion by an attacker. The defender can take defensive actions or stops at different time steps, with the goal of stopping an intrusion. This problem is modeled as a partially observable Markov decision process (POMDP) where the optimal strategy is to determine the optimal times to stop and take defensive actions based on observations over time.

MediaEval 2018: Fine grained sport action recognition: Application to table t...

This document discusses fine-grained sport action recognition applied to table tennis. It introduces two proposed tasks for the MediaEval benchmark: 1) recognizing temporally segmented strokes from a table tennis video and assigning them to one of 21 classes, and 2) performing the same recognition but without temporal segmentation, allowing a 10% error range on boundaries. It also describes the TTStroke-21 dataset created for these tasks, which contains over 1,000 annotated strokes from 129 table tennis videos.

MediaEval 2018: Ensembled Convolutional Neural Network Models for Retrieving ...

Paper: http://ceur-ws.org/Vol-2283/MediaEval_18_paper_27.pdf
Youtube: https://youtu.be/iDwuoVfpDKQ
Yu Feng, Sergiy Shebotnov, Claus Brenner, Monika Sester, Ensembled Convolutional Neural Network Models for Retrieving Flood Relevant Tweets. Proc. of MediaEval 2018, 29-31 October 2018, Sophia Antipolis, France.
Abstract: Social media, which provides instant textual and visual information exchange, plays a more important role in emergency response than ever before. Many researchers nowadays are focusing on disaster monitoring using crowd sourcing. Interpretation and retrieval of such information significantly influences the efficiency of these applications. This paper presents a method proposed by team EVUS-ikg for the MediaEval 2018 challenge on Multimedia Satellite Task. We only focused on the subtask “flood classification for social multimedia”. A supervised learning method with an ensemble of 10 Convolutional Neural Networks (CNN) was applied to classify the tweets in the benchmark.
Presented by Yu Feng

Link-wise Artificial Compressibility Method: a simple way to deal with comple...

The document summarizes the Link-wise Artificial Compressibility Method (LW-ACM), which is a simplified version of the Lattice Boltzmann Method (LBM) for solving fluid flow problems with complex geometries on structured grids. It discusses how LW-ACM modifies the standard Artificial Compressibility Method (ACM) to use a link-wise formulation that borrows ideas from LBM to handle boundaries without needing complex mesh generation. The document provides examples showing LW-ACM can accurately simulate flows like Couette flow and Couette flow with wall injection on simple structured grids.

A FRIENDLY APPROACH TO PARTICLE FILTERS IN COMPUTER VISION

This is a friendly approach to particle filters. Some hints, examples, and good practices to be able to successfully apply particle filters to solve your computer vision pro

Varese italie seminar

Varese italie seminar

Slides ub-1

Slides ub-1

Probabilistic Modelling with Information Filtering Networks

Probabilistic Modelling with Information Filtering Networks

Multimodal Deep Learning

Multimodal Deep Learning

1.IntroDescriptiveDisplay-20222023WS.pdf

1.IntroDescriptiveDisplay-20222023WS.pdf

QMC: Transition Workshop - Selected Highlights from the Probabilistic Numeric...

QMC: Transition Workshop - Selected Highlights from the Probabilistic Numeric...

Selective and incremental re-computation in reaction to changes: an exercise ...

Selective and incremental re-computation in reaction to changes: an exercise ...

“Un modelo basado en agentes para el estudio de la actividad en redes sociale...

“Un modelo basado en agentes para el estudio de la actividad en redes sociale...

Slides ub-2

Slides ub-2

QMC: Undergraduate Workshop, Monte Carlo Techniques in Earth Science - Amit A...

QMC: Undergraduate Workshop, Monte Carlo Techniques in Earth Science - Amit A...

Kernel methods and variable selection for exploratory analysis and multi-omic...

Kernel methods and variable selection for exploratory analysis and multi-omic...

2018 Modern Math Workshop - Foundations of Statistical Learning Theory: Quint...

2018 Modern Math Workshop - Foundations of Statistical Learning Theory: Quint...

Inference via Bayesian Synthetic Likelihoods for a Mixed-Effects SDE Model of...

Inference via Bayesian Synthetic Likelihoods for a Mixed-Effects SDE Model of...

Inventory theory presentation

Inventory theory presentation

Kernel methods for data integration in systems biology

Kernel methods for data integration in systems biology

Learning Intrusion Prevention Policies Through Optimal Stopping

Learning Intrusion Prevention Policies Through Optimal Stopping

MediaEval 2018: Fine grained sport action recognition: Application to table t...

MediaEval 2018: Fine grained sport action recognition: Application to table t...

MediaEval 2018: Ensembled Convolutional Neural Network Models for Retrieving ...

MediaEval 2018: Ensembled Convolutional Neural Network Models for Retrieving ...

Link-wise Artificial Compressibility Method: a simple way to deal with comple...

Link-wise Artificial Compressibility Method: a simple way to deal with comple...

A FRIENDLY APPROACH TO PARTICLE FILTERS IN COMPUTER VISION

A FRIENDLY APPROACH TO PARTICLE FILTERS IN COMPUTER VISION

Using Large Language Models in 10 Lines of Code

Modern NLP models can be daunting: No more bag-of-words but complex neural network architectures, with billions of parameters. Engineers, financial analysts, entrepreneurs, and mere tinkerers, fear not! You can get started with as little as 10 lines of code.
Presentation prepared for the Abu Dhabi Machine Learning Meetup Season 3 Episode 3 hosted at ADGM in Abu Dhabi.

What deep learning can bring to...

... two decades of correlation, hierarchies, networks and clustering in financial markets
Summary of some of my past research work at Complex Networks 2022.
The study of correlations, hierarchies, networks and communities (or clustering) has more than 20 years of history in econophysics.
However, for the practitioner, it seems that these tools are not fully ready yet:
Many questions around their proper use for trading or risk monitoring are left unanswered.
Deep Learning might help solve some hard problems such as finding more reliably communities (or clusters) and their number.
Running large simulations (based on GANs, VAEs or realistic market simulators) could also help understand when complex networks methods can give wrong insights (e.g. not enough data, or not stationary enough; too low correlations).
Conference: Complex Networks 2022 in Palermo, Sicily, Italy.

A quick demo of Top2Vec With application on 2020 10-K business descriptions

A short presentation I did at the Hong Kong Machine Learning Meetup Season 4 Episode 4. Top2Vec is a novel method to find topics in a corpus of documents. It can automatically find a relevant number of topics in the corpus. Besides, you get also relevant word and document vectors for further processing.

How deep generative models can help quants reduce the risk of overfitting?

How deep generative models can help quants reduce
the risk of overfitting? Applications of GANs for Quants.
Presentation at the "QuantUniversity Autumn School 2020".

Generating Realistic Synthetic Data in Finance

Talk at IHS Markit Webinar (15 October 2020) on the potential Applications of GANs in Finance. These models could be useful for quants and their managers to avoid over-fitting, portfolio and risk managers for proper capital and risk allocation, cloud computing servicing willing to work with banks and other sensitive data rich organizations, auditors and regulators to detect frauds, and data vendors (such as IHS Markit) to bring new products to market and iterate quickly with clients.

Applications of GANs in Finance

This presentation highlights potential use cases of deep generative models, and Generative Adversarial Networks (GANs) in particular, in Finance. Essentially, these models are useful to generate realistic synthetic datasets. Quantitative Strategists, Traders, Asset and Risk Managers can find these novel techniques useful. Auditors and Regulators should also become aware of their existence as they may be source of new accounting frauds and misleading financial statements (deepfakes).

My recent attempts at using GANs for simulating realistic stocks returns

A presentation for the Hong Kong Machine Learning meetup summarizing my hobby research over the past year. My goal is to be able to simulate realistic multivariate financial time series. If so, I will be able to compare different statistical methods for portfolio construction, studying complex networks, algorithmic trading, being able to do some reinforcement learning, etc. Still far from being achieved...

Takeaways from ICML 2019, Long Beach, California

The document summarizes takeaways from various talks and presentations at the ICML 2019 conference. It discusses topics like safe machine learning and biases in algorithms, active learning techniques, attention mechanisms in deep learning, differential privacy in census data, time series forecasting methods, Hawkes processes, Shapley values for explainability and data valuation, topological data analysis, optimal transport, applications of machine learning in robotics, Gaussian processes, learning from noisy labels, interpretability methods in NLP, and the GluonTS library for probabilistic time series modeling.

Some contributions to the clustering of financial time series - Applications ...

This document discusses contributions to clustering financial time series, specifically credit default swap data. It introduces credit default swaps and the raw data set. It then discusses challenges in clustering financial time series due to non-stationarity and noisy correlations. It presents initial work on analyzing the consistency of clustering as the sample size increases, through simulations in a simplified setting. Finally, it proposes a two-step approach to proving consistency, by first identifying geometrical configurations that lead to the true clustering structure.

Clustering Financial Time Series using their Correlations and their Distribut...

This document discusses methods for clustering random walks. It introduces the GNPR (Generic Non-Parametric Representation) method for defining a distance between two random walks that separates dependence and distribution information. The GNPR method is shown to outperform standard approaches on synthetic datasets containing different clusters based on distribution and dependence. The GNPR method is also used to cluster credit default swaps, identifying a cluster of "Western sovereigns". The document concludes that GNPR is an effective way to deal with dependence and distribution information separately without losing information.

Optimal Transport vs. Fisher-Rao distance between Copulas

How can we compare two dependence structures (represented by copulas)? It depends on the task. For clustering variables with similar dependence, prefer Optimal Transport. For detecting change points in a dynamical dependence structure, prefer Fisher-Rao and its associated f-divergences (for example, an approach a la Frédéric Barbaresco in radar signal processing). This study illustrates these properties with bivariate Gaussian copulas.

On Clustering Financial Time Series - Beyond Correlation

This document discusses clustering financial time series data using correlation matrices. It summarizes that analyzing 560 credit default swaps over 2500 days, the empirical correlation matrix eigenvalues closely match the theoretical Marchenko-Pastur distribution, indicating noise. Only 26 eigenvalues exceed the theoretical maximum, which may correspond to market and industry factors. Hierarchical clustering can reorder assets to reveal correlation patterns. Filtering by this reveals the underlying network structure. Beyond correlations, copulas represent the dependence structure, and a distance measure is proposed combining L1 and L0 distances of cumulative distribution functions to cluster on full distributions rather than just correlations. Stability tests show the proposed approach yields more robust clusters than standard correlation-based methods.

Optimal Transport between Copulas for Clustering Time Series

Presentation slides of our ICASSP 2016 conference paper in Shanghai. They describe the motivation and design of the Target Dependence Coefficient, a coefficient which can target or forget specific dependence relationships between the variables. This coefficient can be useful for clustering financial time series. Several of such use-cases are described on our Tech Blog https://www.datagrapple.com/Tech/optimal-copula-transport.html

On the stability of clustering financial time series

Talk at IEEE ICMLA 2015 Miami
In this presentation, we suggest some data perturbations that can help to validate or reject a clustering methodology besides yielding insights on the time series at hand. We show in this study that Pearson correlation is not that relevant for clustering these time series since it yields unstable clusters; prefer a more robust measure such as Spearman correlation based on rank statistics.

Clustering Random Walk Time Series

This document discusses clustering random walk time series. It introduces the concept of clustering and discusses challenges in clustering time series, such as how to define a distance between dependent random variables. It proposes using the copula transform and empirical copula transform to map time series to uniform distributions before calculating distances. The hierarchical block model for nested data partitions is presented. Experimental results show the proposed distances perform well in recovering the nested partitions on both synthetic hierarchical block model data and real credit default swap time series data. Consistency of clustering algorithms is defined in relation to recovering the hierarchical block model partitions.

On clustering financial time series - A need for distances between dependent ...

This document discusses clustering financial time series data using distances between dependent random variables. It notes that traditional clustering based only on correlation can lead to spurious clusters, as correlation does not fully capture dependence. The paper proposes a distance measure that combines information about both the correlation and distribution of random variables. It tests this distance measure on synthetic data from a hierarchical block model and real credit default swap market data, finding it performs better than distances based only on correlation or distribution individually. Some open questions are also discussed, such as how to select the optimal weighting of correlation vs distribution information.

Using Large Language Models in 10 Lines of Code

Using Large Language Models in 10 Lines of Code

What deep learning can bring to...

What deep learning can bring to...

A quick demo of Top2Vec With application on 2020 10-K business descriptions

A quick demo of Top2Vec With application on 2020 10-K business descriptions

How deep generative models can help quants reduce the risk of overfitting?

How deep generative models can help quants reduce the risk of overfitting?

Generating Realistic Synthetic Data in Finance

Generating Realistic Synthetic Data in Finance

Applications of GANs in Finance

Applications of GANs in Finance

My recent attempts at using GANs for simulating realistic stocks returns

My recent attempts at using GANs for simulating realistic stocks returns

Takeaways from ICML 2019, Long Beach, California

Takeaways from ICML 2019, Long Beach, California

Some contributions to the clustering of financial time series - Applications ...

Some contributions to the clustering of financial time series - Applications ...

Clustering Financial Time Series using their Correlations and their Distribut...

Clustering Financial Time Series using their Correlations and their Distribut...

Optimal Transport vs. Fisher-Rao distance between Copulas

Optimal Transport vs. Fisher-Rao distance between Copulas

On Clustering Financial Time Series - Beyond Correlation

On Clustering Financial Time Series - Beyond Correlation

Optimal Transport between Copulas for Clustering Time Series

Optimal Transport between Copulas for Clustering Time Series

On the stability of clustering financial time series

On the stability of clustering financial time series

Clustering Random Walk Time Series

Clustering Random Walk Time Series

On clustering financial time series - A need for distances between dependent ...

On clustering financial time series - A need for distances between dependent ...

一比一原版(heriotwatt学位证书)英国赫瑞瓦特大学毕业证如何办理

原版一模一样【微信：741003700 】【(heriotwatt学位证书)英国赫瑞瓦特大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Data Scientist Machine Learning Profiles .pdf

Data Scientist Machine Learning Profiles

reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf

Gran libro. Lo recomiendo

Template xxxxxxxx ssssssssssss Sertifikat.pptx

sssssssssssssssssssssssssssssss

Interview Methods - Marital and Family Therapy and Counselling - Psychology S...

A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!

Q4FY24 Investor-Presentation.pdf bank slide

Bank kotak mahindra bank investors history methods

Drownings spike from May to August in children

Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!

一比一原版爱尔兰都柏林大学毕业证(本硕）ucd学位证书如何办理

原版一模一样【微信：741003700 】【爱尔兰都柏林大学毕业证(本硕）ucd成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024

We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.

[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024

We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.

Telemetry Solution for Gaming (AWS Summit'24)

Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.

一比一原版加拿大麦吉尔大学毕业证（mcgill毕业证书）如何办理

原版一模一样【微信：741003700 】【加拿大麦吉尔大学毕业证（mcgill毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理加拿大麦吉尔大学毕业证（mcgill毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理

原版一模一样【微信：741003700 】【(曼大毕业证书)曼尼托巴大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(曼大毕业证书)曼尼托巴大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

一比一原版莱斯大学毕业证（rice毕业证）如何办理

原版一模一样【微信：741003700 】【莱斯大学毕业证（rice毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理莱斯大学毕业证（rice毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

一比一原版斯威本理工大学毕业证（swinburne毕业证）如何办理

原版一模一样【微信：741003700 】【斯威本理工大学毕业证（swinburne毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
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留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理南昆士兰大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理南昆士兰大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理南昆士兰大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理南昆士兰大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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- 1. Autoregressive Convolutional Neural Networks for Asynchronous Time Series Hong Kong Machine Learning Meetup - Season 1 Episode 1 Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat Imperial College London, Ecole Polytechnique, Hellebore Capital 18 July 2018 HELLEBORECAPITAL Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 1 / 10
- 2. Introduction Problem: Many real-world time series are asynchronous, i.e. the durations between consecutive observations are irregular/random or the separate dimensions are not observed simultaneously. Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 2 / 10
- 3. Introduction Problem: Many real-world time series are asynchronous, i.e. the durations between consecutive observations are irregular/random or the separate dimensions are not observed simultaneously. At the same time: time series models usually require both regularity of observations and simultaneous sampling of all dimensions, continuous-time models often require simultaneous sampling. Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 2 / 10
- 4. Introduction Problem: Many real-world time series are asynchronous, i.e. the durations between consecutive observations are irregular/random or the separate dimensions are not observed simultaneously. At the same time: time series models usually require both regularity of observations and simultaneous sampling of all dimensions, continuous-time models often require simultaneous sampling. Numerous interpolation methods have been developed for preprocessing of asynchronous series. However,... Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 2 / 10
- 5. Drawbacks of synchronous sampling ... every interpolation method leads to either increase in the number of data points or loss of data. 0 20 40 60 80 100 original series Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 3 / 10
- 6. Drawbacks of synchronous sampling ... every interpolation method leads to either increase in the number of data points or loss of data. 0 20 40 60 80 100 original series frequency = 10s; information loss But the situation can be much worse... Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 3 / 10
- 7. Drawbacks of synchronous sampling ... every interpolation method leads to either increase in the number of data points or loss of data. 0 20 40 60 80 100 original series frequency = 10s; information loss frequency = 1s; 12x more points But the situation can be much worse... Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 3 / 10
- 8. Drawbacks of synchronous sampling WLPH SULFH HYROXWLRQRITXRWHGSULFHVWKURXJKRXWRQHGD VRXUFH$ELG VRXUFH$DVN VRXUFH%ELG VRXUFH%DVN VRXUFHELG VRXUFHDVN VRXUFH'ELG VRXUFH'DVN Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 4 / 10
- 9. Drawbacks of synchronous sampling WLPH SULFH HYROXWLRQRITXRWHGSULFHVWKURXJKRXWRQHGD VRXUFH$ELG VRXUFH$DVN VRXUFH%ELG VRXUFH%DVN VRXUFHELG VRXUFHDVN VRXUFH'ELG VRXUFH'DVN Objectives: Propose alternative representation of asynchronous data, Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 4 / 10
- 10. Drawbacks of synchronous sampling WLPH SULFH HYROXWLRQRITXRWHGSULFHVWKURXJKRXWRQHGD VRXUFH$ELG VRXUFH$DVN VRXUFH%ELG VRXUFH%DVN VRXUFHELG VRXUFHDVN VRXUFH'ELG VRXUFH'DVN Objectives: Propose alternative representation of asynchronous data, Find neural network architecture appropriate for such representation. Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 4 / 10
- 11. How to deal with asynchronous data? 0 0.3 1 1.5 1.8 2.7 3.5 4.20.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 value time X Y duration Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 5 / 10
- 12. How to deal with asynchronous data? 0 0.3 1 1.5 1.8 2.7 3.5 4.20.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 value time X Y duration X indicator value Y indicator duration Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 5 / 10
- 13. How to deal with asynchronous data? 0 0.3 1 1.5 1.8 2.7 3.5 4.20.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 value time X Y duration 1 4.0 0 .3 X indicator value Y indicator duration Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 5 / 10
- 14. How to deal with asynchronous data? 0 0.3 1 1.5 1.8 2.7 3.5 4.20.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 value time X Y duration 1 4.0 7.5 0 0 1 .3 .7 X indicator value Y indicator duration Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 5 / 10
- 15. How to deal with asynchronous data? 0 0.3 1 1.5 1.8 2.7 3.5 4.20.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 value time X Y duration 1 1 4.0 7.5 0 0 1 .3 .7 9.0 2.3 0 1 1 0 .5 .3 7.7 5.0 1 0 0 1 .9 .6 4.5 5.1 1 0 0 .7 1.3 X indicator value Y indicator duration Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 5 / 10
- 16. Not satisfactory performance of Neural Nets Architectures such as Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) do not perform as well as expected, compared to simple autoregressive (AR) model Xn = M m=1 Xn−m × am + εn (1) Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 6 / 10
- 17. Not satisfactory performance of Neural Nets Architectures such as Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) do not perform as well as expected, compared to simple autoregressive (AR) model. Idea: equip AR model with data-dependent weights Xn = M m=1 Xn−m × am(Xn−m) + εn (1) Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 6 / 10
- 18. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 19. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 20. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 21. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 22. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Offset networkSignificance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 23. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Convolution kx1 kernel c channels Convolution 1x1 kernel c channels Offset networkSignificance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 24. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors × (𝑵 𝑺 − 𝟏) layers Convolution kx1 kernel c channels × (𝑵 𝒐𝒇𝒇 − 𝟏) layers Convolution 1x1 kernel c channels Offset networkSignificance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 25. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors × (𝑵 𝑺 − 𝟏) layers Convolution kx1 kernel c channels Convolution 1x1 kernel dI channels Convolution kx1 kernel dI channels × (𝑵 𝒐𝒇𝒇 − 𝟏) layers Convolution 1x1 kernel c channels Offset networkSignificance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 26. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors × (𝑵 𝑺 − 𝟏) layers Convolution kx1 kernel c channels Convolution 1x1 kernel dI channels Convolution kx1 kernel dI channels × (𝑵 𝒐𝒇𝒇 − 𝟏) layers Convolution 1x1 kernel c channels Offset network 𝒙𝑰 Significance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps 𝐨𝐟𝐟 Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 27. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Weighting 𝑯 𝒏−𝟏 = 𝝈 𝑺 ⨂ (𝐨𝐟𝐟 + 𝒙 𝑰 ) × (𝑵 𝑺 − 𝟏) layers Convolution kx1 kernel c channels 𝑺 𝛔 Convolution 1x1 kernel dI channels Convolution kx1 kernel dI channels × (𝑵 𝒐𝒇𝒇 − 𝟏) layers Convolution 1x1 kernel c channels Offset network 𝒙𝑰 Significance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps 𝐨𝐟𝐟 Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 28. Proposed Architecture The model predicts yn = E[xI n|x−M n ], where x−M n = (xn−1, . . . , xn−M) - regressors I = (i1, i2, . . . , idI ) - target dimensions with ˆyn = M m=1 W·,m ⊗ σ(S(x−M n ))·,m data dependent weights ⊗ oﬀ(xn−m) + xI n−m adjusted regressors Weighting 𝑯 𝒏−𝟏 = 𝝈 𝑺 ⨂ (𝐨𝐟𝐟 + 𝒙 𝑰 ) × (𝑵 𝑺 − 𝟏) layers Convolution kx1 kernel c channels 𝑺 𝛔 Convolution 1x1 kernel dI channels Convolution kx1 kernel dI channels × (𝑵 𝒐𝒇𝒇 − 𝟏) layers Convolution 1x1 kernel c channels Offset network 𝒙𝑰 Significance network Input series 𝒙 𝒕−𝟔 𝒙 𝒕−𝟓 𝒙 𝒕−𝟒 𝒙 𝒕−𝟑 𝒙 𝒕−𝟐 𝒙 𝒕−𝟏 d - dimensional timesteps Locally connected layer fully connected for each of 𝒅𝑰 dimensions 𝑯 𝒏 = 𝑾𝑯 𝒏−𝟏 + 𝒃 𝐨𝐟𝐟 ෝ𝒙 𝒕 𝑰 Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 7 / 10
- 29. Experiments Datasets: artiﬁcially generated, synchronous asynchronous Electricity consumption [UCI repository] Quotes [16 tasks] Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 8 / 10
- 30. Experiments Datasets: artiﬁcially generated, synchronous asynchronous Electricity consumption [UCI repository] Quotes [16 tasks] Benchmarks: (linear) VAR model vanilla LSTM, 1d-CNN 25-layer conv. ResNet Phased LSTM [Neil et al. 2016] Sync 16 Sync 64 Async 16 Async 64 Electricity Quotes0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 MSE VAR CNN ResNet LSTM Phased LSTM SOCNN (ours) Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 8 / 10
- 31. Experiments #2 Ablation study: Signiﬁcance Network needs more depth than the Oﬀset Past observations are pretty good predictors, we just need to weight them Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 9 / 10
- 32. Experiments #2 Ablation study: Signiﬁcance Network needs more depth than the Oﬀset Past observations are pretty good predictors, we just need to weight them Robustness: What happens to the error if we add noise to the input? DGGHGQRLVHLQVWDQGDUGGHYLDWLRQV
- 33. PVH WUDLQVHW 11 /670 /670 6211 VLJQLILFDQFH _RIIVHW_ DGGHGQRLVHLQVWDQGDUGGHYLDWLRQV
- 34. PVH WHVWVHW 11 /670 /670 6211 VLJQLILFDQFH _RIIVHW_ The proposed model seems to be more robust. Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 9 / 10
- 35. Code: https://github.com/mbinkowski/nntimeseries Thank you for your attention! Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 10 / 10