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Search results “Empirical time series analysis”
Types of data, time series data, cross sectional data and pooled data
 
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In this video tutorial you will learn Types of data and sources of data for empirical analysis. In types of data there are three types, which we discussed in this tutorial. The time series data, cross sectional data and pooled data are discussed one by one. Some of the sources for collecting the data are also discussed in this tutorial. For more details log on to http://economicsguider.com/.
Views: 6968 Economics Guider
Applications of Time Series Analysis
 
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Statistics and Data Series presentation by Dr. Ivan Medovikov, Economics, Brock University, Apr. 17, 2013 at The University of Western Ontario: "Applications of Time Series Analysis" This is a follow-up to "Introduction to Time Series Analysis" presented by Ivan Medovikov in the 2011-2012 Statistics and Data Series. The talk focussed on several applied problems which arise in time-series analysis, particularly, the problem of model-selection and testing for goodness of fit, the issues surrounding data with seasonal trends, and the problem of time-series forecasting. Slides for this presentation are on the RDC website. The Statistics and Data Series is a partnership between the Centre for Population, Aging and Health and the Research Data Centre. This interdisciplinary series promotes the enhancement of skills in statistical techniques and use of quantitative data for empirical and interdisciplinary research. More information at http://rdc.uwo.ca Look for more events like this on the Sociology Events Calendar. Uploaded by Communications and Public Affairs in 2014
Views: 37627 Western University
Time Series analysis
 
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Watch this brief (10 minutes or so!!) video tutorial on how to do all the calculations required for a Time Series analysis of data on Microsoft Excel. Try and do your best to put up with the pommie accent. The data for this video can be accessed at https://sites.google.com/a/obhs.school.nz/level-3-statistics-and-modelling/time-series
Views: 104153 mrmathshoops
Econometrics for Finance - S5 - Univariate Time Series - Modeling and Forecasting
 
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Here we model and predict financial variables using only information contained in their own past and values and possibly current and past values of an error term. The BSU E-learning and Problem-based Learning Programme seeks to strengthen the capacity of South Universities including University of Ghana in the using of e-learning and problem-based learning approach for teaching and learning in UG. This video-lecture is developed under this initiative to enhance learning activities in the classroom and beyond.
Auto Correlation Function in Time Series Analysis | Foresting
 
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In this video you will learn what is Auto correlation function and what is it used for in time series analysis For Analytics Study Pack visit : http://analyticuniversity.com/ For training, mentorship contact us at [email protected] Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx
Views: 32171 Analytics University
Demand Estimation
 
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Using Excel to estimate demand from study data
Views: 11738 rick loutzenhiser
Equation and parameter free dynamical modeling of natural time series
 
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This video gives a cursory overview of the tools for natural time series analysis developed by the Sugihara lab at Scripps Institution of Oceanography (UCSD). The methods discussed give an introduction to Takens theorem and follow the principles of avoiding equations and free parameters so as to avoid overfitting. All approaches use principles from nonlinear dynamics and chaos theory and are numerical rather than equation based. For more information on the subject follow the links below. http://deepeco.ucsd.edu/ https://www.quantamagazine.org/20151013-chaos-theory-and-ecology/ http://www.pnas.org/content/112/13/3856.full http://www.ncbi.nlm.nih.gov/pubmed/22997134
Understanding Wavelets, Part 1: What Are Wavelets
 
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This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 144102 MATLAB
Principal component analysis
 
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Currell: Scientific Data Analysis. Minitab analysis for Figs 9.6 and 9.7 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
An Overview of Signal Denoising
 
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This presentation covers signal filtering and analysis tools for recovering important information from noise-corrupted signals. For more information on EMD: N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis," Proc. R. Soc. London, vol. Ser. A, 454, pp. 903-995, 1998.
Views: 1805 Jordan Smith
Hurst time series with memory compared with Gaussian series
 
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Reference: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763358 The series on the right is pure Gaussian noise with no memory and it is created anew for each frame. The series on the left is derived from the Gaussian series by adding a probability of 10% that the direction of the change will persist. This degree of persistence corresponds with a Hurst exponent of H=0.8, much greater than the Hurst exponent of the Gaussian series computed as H-0.54 under the same empirical conditions.
Views: 583 Jamal Munshi
Introduction to Time Series Modeling and Analysis : Stylized Facts
 
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Stylized facts represent the first step of a time serie modeling ! If you have any questions don't hesistate to contact me : https://web.facebook.com/AmenFlint Peace.
Views: 146 Returning the Favor
Introduction to Panel Data Analysis
 
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Statistics and Data Series presentation by Dr. Youngki Shin, Nov 21, 2012 at The University of Western Ontario: "Introduction to Panel Data Analysis." The presentation introduced basic techniques of analysis of panel data, which are typically collected over time for the same individuals. Regression models, with both fixed and random effects, were discussed and illustrated using Stata. A simple statistical test for choosing between the fixed effect estimation and the random effect estimation was also covered. Slides for this presentation are online at the RDC website. The Statistics and Data Series is a partnership between the Centre for Population, Aging and Health and the Research Data Centre. This interdisciplinary series promotes the enhancement of skills in statistical techniques and use of quantitative data for empirical and interdisciplinary research. More information at http://rdc.uwo.ca
Views: 73947 Western University
Quantlab - Machine Learning cont. w. Timeseries
 
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A continuation of the previous Machine Learning example. Here we use our Neural Net code on historical financial timeseries to get a prediction of the future in our target exchange rate. Note that this is just an example of using the Neural Net for timeseries and not an actual predictor of the exchange rate.
Views: 160 Quantlab
Scalable Clustering of Correlated Time Series using Expectation Propagation
 
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Author: Christopher Aicher, Department of Statistics, University of Washington Abstract: We are interested in finding clusters of time series such that series within a cluster are correlated and series between clusters are independent. Existing Bayesian methods for inferring correlated clusters of time series either: (i) require conditioning on latent variables to decouple time series, but results in slow mixing or (ii) require calculating a collapsed likelihood, but with computation scaling cubically with the number of time series per cluster. To infer the latent cluster assignments efficiently, we consider approximate methods that trade exactness for scalability. Our main contribution is the development of an expectation propagation based approximation for the collapsed likelihood approach. Our empirical results on synthetic data show our methods scale linearly instead of cubically, while maintaining competitive accuracy. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 585 KDD2016 video
Modeling multivariate time series in economics: Autoregressions versus Recurrent Neural Networks
 
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On August 23-24, 2018 the CMSA hosted our fourth annual Conference on Big Data. The Conference featured many speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics. Speaker: Sergiy Verstyuk Title: Modeling multivariate time series in economics: Autoregressions versus Recurrent Neural Networks Abstract: Non-structural empirical modeling is important in economics. It is used extensively for such tasks as forecasting and policy analysis. I apply vector autoregression and multivariate recurrent neural network methods to economic variables and compare their results.
Views: 101 Harvard CMSA
Constructing Empirical Dynamic Models: Taken' Theorem
 
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This movie illustrates how Takens' Theorem can be used to reconstruct a shadow manifold M_x from a single time series, and illustrates the 1:1 mapping between M and M_x. from: "Detecting Causality in Complex Ecosystems" (Science DOI: 10.1126/science.1227079 (2012)). George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Mike Fogarty, and Stephan Munch. narration by: Robert M. May
Views: 1263 Sugihara Lab
State Space Reconstruction: Time Series and Dynamic Systems
 
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This movie demonstrates the relationship between time series and dynamic attractors (manifolds, M). Movie S1. A supplemental simulation and animation for "Detecting Causality in Complex Ecosystems" (Science DOI: 10.1126/science.1227079 (2012)). George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Mike Fogarty, and Stephan Munch. animation by: Peter Sugihara, Hao Ye, and George Sugihara For more publications applying state space reconstruction, please visit http://www.sio.ucsd.edu/Profile/gsugihara
Views: 6005 Sugihara Lab
Non-stationarity: a hazard for forecasting
 
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Professor Sir David Hendry discusses his new policy paper, 'All Change! The Implications of Non-Stationarity for Empirical Modelling, Forecasting and Policy', co-written with Dr Felix Pretis. Read more at http://www.oxfordmartin.ox.ac.uk/admin/news/201611_All_Change Oxford Martin School, University of Oxford www.oxfordmartin.ox.ac.uk
[Python] Reconstruction of The Lorenz Attractor | Chaotic Time Series Analysis
 
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Reconstruction of The Lorenz Attractor by Takens Embedding Theorem music : Dan Isik BEΛTS - FREE ♪ "My Show" [ Vocal / Guitar / Piano / Emotional ] https://soundcloud.com/dkk993/free-my-show-vocal-guitar-piano-emotional
Views: 568 Keiji Namba
Principal Components Analysis - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-649069103/m-661438544 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 252838 Udacity
4 4 Diagnostic Tests
 
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quantedu.com/wp-content/uploads/2014/04/Time Series/4_4Test
Views: 2652 Quant Education
Time Series Bootstrap - Statistical Inference
 
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In this video I talk about bootstrap being applied to time series where we explore the topic through the question: what is my hydration over time?
Views: 263 Data Talks
White Noise Process | Time Series
 
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IN this video you will learn what is a white noise process For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all free videos & study packs available with us here: http://analyticuniversity.com/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
Views: 15952 Analytics University
Estimating a VAR(p) in EVIEWS
 
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This clip demonstrates some basic EVIEWS techniques used to estimate Vector Autoregressive Models. If you are after the theory of VARs you may want to look at these clips VAR Setup, Representations, Properties: http://youtu.be/cw0hi00Yieg VAR Estimation and Uses: http://youtu.be/J6BTw2Ff95A Data used, you can download data like these from the Federal reserves FRED database, eg. French data are on: https://research.stlouisfed.org/fred2/series/IRLTLT01FRM156N (slightly different to the data used in the clip though)
Views: 139200 Ralf Becker
How to create time plots in SPSS | lynda.com tutorial
 
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This SPSS tutorial shows how to create a time series scatter plot chart. Watch more at http://www.lynda.com/SPSS-19-tutorials/SPSS-Statistics-Essential-Training/83838-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-83838-0703 This specific tutorial is just a single movie from chapter seven of the SPSS Statistics Essential Training course presented by lynda.com author Barton Poulson. The complete SPSS Statistics Essential Training course has a total duration of 5 hours, and covers the basics of statistical analysis in SPSS, including importing spreadsheets, creating regression models, exporting presentation graphics, and more SPSS Statistics Essential Training table of contents: Introduction 1. Getting Started 2. Charts for One Variable 3. Modifying Data 4. Working with the Data File 5. Descriptive Statistics for One Variable 6. Inferential Statistics for One Variable 7. Charts for Two Variables 8. Descriptive and Inferential Statistics for Two Variables 9. Charts for Three or More Variables 10. Descriptive Statistics for Three or More Variables 11. Formatting and Exporting Tables and Charts Conclusion
Views: 57225 LinkedIn Learning
Combining Multivariate Time Series and Derivatives Analytics
 
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Yves Hilpisch illustrates the use of advanced Vector Autoregression techniques to forecast parameter time series of option pricing models. He uses the Python-based financial analytics library http://dx-analytics.com to implement the derivatives pricing.
Views: 769 Yves Hilpisch
Bootstrap Resampling
 
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This video provides an introduction to the technique of bootstrap resampling, which is a computational method of measuring the error in a statistic's estimator.
Views: 97615 Nick Hand
Introductory Econometrics: Wooldridge Book Review
 
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This book covers a large number of topics that will be useful for statistics, risk management, and econometrics. The book does a great job at explaining technical details, discusses model development in a linear fashion, and makes it easy to find specific topics quickly. Rating: 5/5 Book on Amazon: https://www.amazon.com/Introductory-Econometrics-Approach-Wooldridge-Hardcover/dp/B00BR5FKUC/ref=sr_1_1_a_it?ie=UTF8&qid=1512841547&sr=8-1&keywords=wooldridge+5th+edition Ch 1) The Nature of Econometrics and Economic Data Part 1: Regression Analysis with Cross-Sectional Data Ch 2) The Simple Regression Model Ch 3) Multiple Regression Analysis: Estimation Ch 4) Multiple Regression Analysis: Inference Ch 5) Multiple Regression Analysis: OLS Asymptotics Ch 6) Multiple Regression Analysis: Further Issues Ch 7) Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables Ch 8) Heteroskedasticity Ch 9) More on Specification and Data Problems Part 2: Regression Analysis with Time Series Data Ch 10) Basic Regression Analysis with Time Series Data Ch 11) Further Issues in Using OLS with Time Series Data Ch 12) Serial Correlation and Heteroskedasticity in Time Series Regressions Part 3: Advanced Topics Ch 13) Pooling Cross Sections Across Time. Simple Panel Data Methods Ch 14) Advanced Panel Data Methods Ch 15) Instrumental Variables Estimation and Two Stage Least Squares Ch 16) Simultaneous Equations Models Ch 17) Limited Dependent Variable Models and Sample Selection Corrections Ch 18) Advanced Time Series Topics Ch 19) Carrying out an Empirical Project Buy the book here: https://amzn.to/2Lc8Nuw (affiliate link) DISCLAIMER: This description contains affiliate links which means that if you click on one of the product links, I’ll receive a small commission for driving traffic to Amazon. Affiliate links help support this channel and allows me to continue to make videos like this. Thank you for the support this channel!
Views: 923 Dimitri Bianco
NIPS 2015 Workshop (Mannor) 15509 Time Series Workshop
 
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Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme, some online learning algorithms consider a non-stochastic framework without any distributional assumptions. However, such methods may fail to fully address the stochastic aspect of real-world time-series data. lt br gt lt br gt The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series, and the development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.
Views: 172 NIPS
Empirical Analysis and Forecast of Electricity Demand in West African Economic and Monetary Zone Evi
 
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Empirical Analysis and Forecast of Electricity Demand in West African Economic and Monetary Zone Evidence from Panel ADRL Modelling
Views: 2 Research Media
The Engle-Granger Two-Step Cointegration Analysis in OxMetrics
 
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We show how to perform the Engle-Granger two-step cointegration analysis in OxMetrics. We consider an empirical example with the Danish money market interest rate and the bond yield over the sample of quarterly data from 1971:1 to 2003:2. We reproduce the results presented on slides 28-31 in the lecture slides on Cointegration. First, we consider the case of a known cointegration relation given by the interest rate spread. We test for a unit root in the spread using the Augmented Dickey-Fuller (ADF) test. We reject the null of a unit root in the spread, so we reject the null of no cointegration. Second, we estimate the cointegration parameter and test for a unit root in the estimated residuals from a static regression of the short rate on a constant and the bond yield. We reject the null of a unit root in the estimated residuals from the static regression, so we reject the null of no cointegration. Finally, we estimate an error-correction model (ECM) for both of the two variables, where we include the fixed cointegration relation given by the spread in the model. We find that only the short interest rate error-corrects as the adjustment coefficient has the right sign and is significant. By contrast the adjustment coefficient for the bond yield is insignificant and with the opposite sign.
Views: 4815 Morten Nyboe Tabor
How to interpret regression tables
 
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This video is for students who have had some exposure to regression methods, but need a refresher on how to interpret regression tables.
Views: 36628 Doug McKee
Dynamic Social Network Analysis: Model, Algorithm, Theory, & Application CMU Research Speaker Series
 
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Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time, or over a genealogy. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this talk, I will present some recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will first present a new class of statistical models known as dynamic exponential random graph models for evolving social networks, which offers both good statistical property and rich expressivity; then, I will present new sparse-coding algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; finally, I will present a new Bayesian model for estimating and visualizing the trajectories of latent multi-functionality of nodal states in the evolving networks. I will show some promising empirical results on recovering and analyzing the latent evolving social networks in the US Senate and the Enron Corporation at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks.
Views: 2650 Microsoft Research
GEL Estimation for Semi-Strong Non-Linear GARCH with Robust Empirical Likelihood Inference
 
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Лекция: GEL Estimation for Semi-Strong Non-Linear GARCH with Robust Empirical Likelihood Inference | Лектор: Артем Прохоров | Организатор: Европейский университет в Санкт-Петербурге Смотрите это видео на Лекториуме: https://www.lektorium.tv/lecture/15141 Подписывайтесь на канал: https://www.lektorium.tv/ZJA Следите за новостями: https://vk.com/openlektorium https://www.facebook.com/openlektorium
Views: 291 Лекториум
COCOMO Model – Constructive Cost Model Introduction - Software Engineering Lectures
 
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COCOMO Model – Constructive Cost Model Introduction - Software Engineering Lectures Hindi and English
Graph-based Multivariate Time Series Analysis
 
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오늘 간만에 포스팅하는 주제는 이전 인공지능 주제들과는 다소 차이가 있다. 학습 기법과 모델에 대한 주제가 아닌, 시간의 흐름에 따라 동시다발적으로 생성되는 데이터를 "분석" 하는 기법에 대한 포스팅이다. 따라서 학습에 대한 내용은 포함되어 있지 않다. 주제는, 다양한 종류의 데이터가 시간의 흐름에 따라 Time Series로 생성될 때, 각 종류의 데이터 사이의 관계를 분석해주는 Time Series Data를 Graph의 형태로 변환하여 분석하는 방법과 그 적용 사례들에 대해 다루고자 한다. 영상의 목차는 아래와 같다: * References - 어떤 논문 및 웹페이지를 참고하였는가 * Background - 왜 이 주제를 다루게 되었는가? * Motivation: Why Graph-based Analysis? - 왜 graph 기반으로 분석을 수행하는가? * Prerequisite: Visibility Algorithm - Multivariate Time Series 데이터의 Graph로의 변환은 어떻게 수행되는가? * Graph-based "Multivariate" Time Series - graph 기반의 분석은 어떻게 수행되는가? * Case Study: Coupled Map Lattices (CML) - 실제 적용 사례: CML * Case Study: Application to Deep Learning (RNN) Analysis and Optimization - 실제 적용 사례: RNN타입의 Echo State Network 최적화 * Lesson Learned - 배운점
Views: 341 Heesuk Son
Simulation Modeling | Tutorial #14 | Time series input model : EAR (1)
 
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This video is about the concept and algorithm on the Exponential Autoregressive model of order 1 in the context of simulation modeling for time series models ويتعلق هذا الفيديو بالمفهوم والخوارزمية على نموذج الانحدار الذاتي الأسي للترتيب 1 في سياق نمذجة المحاكاة لنماذج السلاسل الزمنية Cette vidéo porte sur le concept et l'algorithme sur le modèle Exponential Autoregressive de l'ordre 1 dans le cadre de la modélisation de simulation pour les modèles de séries temporelles Bei diesem Video geht es um das Konzept und den Algorithmus auf dem exponentiellen Autoregressiven Modell der Ordnung 1 im Rahmen der Simulationsmodellierung für Zeitreihenmodelle Bei diesem Video geht es um das Konzept und den Algorithmus auf dem exponentiellen Autoregressiven Modell der Ordnung 1 im Rahmen der Simulationsmodellierung für Zeitreihenmodelle Это видео посвящено концепции и алгоритму модели экспоненциальной авторегрессии порядка 1 в контексте имитационного моделирования для моделей временных рядов Este video trata sobre el concepto y algoritmo en el modelo exponencial autoregresivo de orden 1 en el contexto del modelado de simulación para modelos de series temporales Add me on Facebook 👉 https://www.facebook.com/renji.nair.09 Follow me on Twitter 👉 https://twitter.com/iamRanjiRaj Like TheStudyBeast on Facebook 👉 https://www.facebook.com/thestudybeast/ For more videos LIKE SHARE SUBSCRIBE and don't forget to SHARE ! HAPPY LEARNING !
Views: 2644 Ranji Raj

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