Search results “Singular spectrum analysis forecasting”
GG413: Introduction to Spectral Analysis
University of Hawaii, Dept. of Geology & Geophysics, Garrett Apuzen-Ito, GG413: Geological Data Analysis www.soest.hawaii.edu/GG/FACULTY/ITO/GG413
Views: 17558 Garrett Apuzen-Ito
Time Series Analysis (Georgia Tech) - 5.1.2 - Spectral Analysis - Introduction
Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 5: Other Time Series Methods Part 1: Univariate Time Series Modelling Lesson: 2 - Spectral Analysis - Introduction Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 679 Bob Trenwith
8. Time Series Analysis I
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 186888 MIT OpenCourseWare
Singular Spectrum Analysis A New Tool in Time Series Analysis Paperback
Singular Spectrum Analysis A New Tool in Time Series Analysis Paperback.cbooks.club
Views: 922 Lois Bennett
Spectral analysis
Currell: Scientific Data Analysis. SPSS and Minitab analyses for Figs 7.16 and 7.18 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
The Herglotz's Theorems & Some Applications
We first recall two classical theorems of Gustav Herglotz on integral representations of positive harmonic functions and of holomorphic functions with positive real part. As a first application, we present a short proof of Caratheodory's Theorem for characterization of holomorphic functions with positive real part in terms of the Toeplitz kernel of the Taylor coefficients of the functions. After recalling the Stieltjes Inversion Formula we provide a second application to the spectral theory of linear operators in indefinite inner product spaces. Analysis Seminars Prof. Aurelian Gheondea Department of Mathematics Bilkent University #Mathematics #BilkentUniversity #AurelianGheondea
Dynamic Mode Decomposition for Univariate Time Series: Analysing Trends and Forecasting
Trends and complete reconstruction using increasing DMD pairs on publicly available Box and Jenkins airline data. Paper with comparison to singular spectrum analysis is available at: https://hal.archives-ouvertes.fr/hal-01463744/document
Views: 394 Santosh Tirunagari
Dynamical systems analysis of climate variability, lecture 1/6
Dynamical systems analysis of climate variability, lecture 1/6 by Henk Dijkstra
Реализовал метод V-SSA. Протестировал динамику работы со следующими параметрам: длина интервала - 600 длина окна - 200 точек прогнозирования - 60 первых собственных векторов SVD разложения - 15 Красный график это реальные минутные данные газпрома ммвб от 16.06.2010. Зеленый график до синей галочки это трендовая составляющая разложения реального временного ряда. После синей галочки это предсказание. Т.е. зеленый график знает про красный только до синей линии, а потом угадывает. Алгоритм достаточно требователен к ресурсам. Без оптимизаций 2-3 секунды на одну итерацию.
Views: 399 Anatoly Ivanov
Time Series Analysis (Georgia Tech) - 5.1.3 - Spectral Analysis - Spectral Density and Covariance Fn
Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 5: Other Time Series Methods Part 1: Univariate Time Series Modelling Lesson: 3 - Spectral Analysis - Spectral Density and Covariance Functions Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 213 Bob Trenwith
Time Frequency Analysis & Wavelets
COURSE WEBPAGE: Inferring Structure of Complex Systems https://faculty.washington.edu/kutz/am563/am563.html This lecture introduces the wavelet decomposition of a signal. The time-frequency decomposition is a generalization of the Gabor transform and allows for a intuitive decomposition of time series data at different frequencies.
Views: 17643 Nathan Kutz
SPSS v.23 Lesson 93: Spectral Analysis  حصريًا باللغة العربية التحليل الطيفي
حصريًا باللغة العربية الدرس 93 من سلسلة دروس د / أسماء الميرغني لشرح البرنامج الإحصائي SPSS v.23 .. Spectral Analysis التحليل الطيفي .. الزمن : 15 دقيقة .. الحقوق الفكرية محفوظة لقناتي يوتيوب : قناة أسماء الميرغني تويتر : Dr_Asmaa1977 فيس بوك : smaa1977 لا أسمح بنسخ المواد العلمية الخاصة بي على أي أسطوانة وبيعها تحت أي شعار كالجامعات والمعاهد أو المؤسسات البحثية والمراكز التدريبية في أي مكان بأسعار رمزية أو غير رمزية لكن أسمح بمشاركة الروابط في أي موقع. السبت 25 مايو 2017 م
Spectrum Graph with Excel 2010,2007
How to make a Spectrum Graph with Excel, brought to you by Frank, The Commons, MUN
Views: 26153 TheCommons MUN
Poster Highlights (Short Presentations)
Authors: Anna Mándli, Robert Bosch LLC. Rui Li, SAS Institute Inc. Pu Wang, SAS Institute Inc. Abstract: Pu Wang: Automatic Singular Spectrum Analysis and Forecasting The singular spectrum analysis (SSA) method of time series analysis applies nonparametric techniques to decompose time series into principal components. SSA is particularly valuable for long time series, in which patterns (such as trends and cycles) are difficult to visualize and analyze. An important step in SSA is determining the spectral groupings; this step can be automated by analyzing the w-correlations (weighted correlations) of the spectral components. To illustrate, monthly data on temperatures in the United States for about the last 100 years are analyzed to discover significant patterns. Rui Li: Short-Term Wind Energy Forecasting with Temporally Dependent Neural Network Models As the penetration of renewable energy into the electrical grid is increasing worldwide, accurate forecasting of renewable energy generation is essential not only for grid operation and reliability, but also for energy trading and long-term planning. In this paper, we focus on short-term wind energy forecasting. The inherent variability and unpredictability of wind energy imposes great challenges upon many models. Conventional time series models, such as ARIMAX, often fail to capture nonlinear patterns in energy output, and a feedforward artificial neural network doesn’t take temporal dependency into account. In this paper, we apply state-of-art autoregressive artificial neural network (AR-ANN) models and recurrent neural network (RNN) models to wind energy forecasting. By capturing both the sequential pattern of energy output and the complex relationship between weather predictors and power generation, we can achieve better forecasting accuracy. These temporally dependent neural network structures can also be easily extended to model other nonlinear time series and temporal data. Anna Mándli: Time Series Classification for Scrap Rate Prediction in Transfer Molding In this paper, we present and evaluate methods for predicting critical increase in manufacturing scrap rate of automotive electronic products. Along with information on processes such as maintenance cycles, we analyze the sensor time series of the so-called transfer molding process, in which the electronic product is packaged into plastic for protection. Production data are organized in a two level hierarchy of the individual parts and of the sequence of parts. Since the main goal is to predict and warn about the future state of the process, we designed a training and prediction framework over certain production cycles. By using sensor and other information, we adapt known time series classifi- cation methods to predict increase in scrap rate in the near future. By using three months of manufacturing time series, we evaluate both feature based and dynamic time warping based methods that are capable of fusing a large number of production time series. As a main conclusion, we may warn the operators of increase in failures with an AUC above 0.7 by combining multiple approaches in our final classifier ensemble. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 24 KDD2017 video
Dietmar Dommenget EOF lecture Pt1
Dr Dietmar Dommenget's lecture on EOF
Views: 1027 ARC CLEX
Dynamic Mode Decomposition (Overview)
In this video, we introduce the dynamic mode decomposition (DMD), a recent technique to extract spatio-temporal coherent structures directly from high-dimensional data. DMD has been widely applied to systems in fluid dynamics, disease modeling, finance, neuroscience, plasma physics, robotics, and video modeling. http://dmdbook.com/ https://www.eigensteve.com/
Views: 6734 Steve Brunton
RF Spectrum Analysis ~ Is It Worth The While? - Peter Sierck
Featured Presentation: RF Spectrum Analysis ~ Is It Worth The While?, delivered at the 2015 Building Biology Conference: Ecological Prescriptions For Healthy Families
Time Series Analysis - Spectral Analysis_วิเคราะห์สเปคตรัม
Time Series Analysis - Spectral Analysis_วิเคราะห์สเปคตรัม โดย ดร.ฐณัฐ วงศ์สายเชื้อ (Thanut Wongsaichue, Ph.D.) เนื้อหาที่ upload แล้ว สถิติ งานวิจัย ความแตกต่าง สมการกำลังสอง โปรแกรม SPSS การถดถอยอย่างง่าย ตัวแปรกลุ่ม ตัวแปรอันดับ ข้อมูลอ่อน Soft Data ตัวแปรกวน Confounding factor การทำความสะอาดข้อมูล Data Cleaning การวิเคราะห์ข้อมูล Data Analysis งานวิจัย Research ทดสอบตัวอย่างอคติ Sample selection bias การสร้างแฟ้มข้อมูล การแก้ไขแฟ้มข้อมูล การถ่วงน้ำหนัก การยุบกลุ่ม ความแตกต่าง สถิติเปรียบเทียบ ค่าเฉลี่ย Mean ถดถอยพหุคูณ ถดถอยพหุ Multiple Regression ถดถอย Simple Regression สหสัมพันธ์ Correlation ไคสแควร์ Chi-square สถิติที t-test การเปรียบเทียบค่าเฉลี่ย ANOVA, f-test ทดสอบความแปรปรวน การวิเคราะห์แบบจำลองสมการโครงสร้าง SEM Structural Equation Modeling ใน AMOS โมเดลสมการโครงสร้าง CFA การวิเคราะห์องค์ประกอบเชิงยืนยัน การจำแนกองค์ประกอบ EFA การวิเคราะห์ถดถอยโลจิสติกส์ Logistic Regression, Logit Analysis, Multicollinearity, Collinearity, Z score, Mediator variable, Square root, Log transform, Log 10, Log N, Natural Log, Power transform, square, cube, quadratic, creating variable, สมการถดถอยพหุ, ค่าสมบูรณ์ Absolute value, ตัวแปรกลุ่ม Categorical variable, Dichotomous, Auto-correlation, กลุ่มอ้างอิง reference category, ถดถอยปัวซอง, ถดถอยพัวซอง, Poisson regression, Factor analysis, การวิเคราะห์องค์ประกอบ
Views: 360 Thanut Wongsaichue
Dynamic Mode Decomposition (Code)
In this video, we code up the dynamic mode decomposition (DMD) in Matlab and use it to analyze the fluid flow past a circular cylinder at low Reynolds number. Code and data available at: http://dmdbook.com/ https://www.eigensteve.com/
Views: 2752 Steve Brunton
BiNeuro  [BNR] - Blockchain inside NeuroAdvertising Ecosystem
Our company have been developing and using the in-house version of BiNeuro since 2009. On the advertising campaigns of our clients, BiNeuro shows a stable increase in the campaign's performance by at least 50%. BiNeuro is an artificial intelligence system built on Data Science Technology: machine learning (neural networks), Big Data, Singular Spectrum Analysis (SSA), fuzzy logic and genetic algorithms. The need to manage the multi-factor process in a qualitative way leads the company to a significant increase in the cost of organizing advertising campaigns, and this still does not save us from ineffectiveness. Scaling BiNeuro by transferring to WEB interface that allows a small advertising agency or freelancer, independently and effectively carry out online advertising campaigns for small and large customers, is able to reformat the market with volume of $ 220.83 billion in a short time. 🌐 ICO Website https://bineuro.com 🌐 ICO Whitepaper https://bineuro.com/web/WhiteP.pdf 🌐 ICO BitcoinTalk ANN https://bitcointalk.org/index.php?topic=3229031.0 🌐 ICO Telegram https://t.me/BiNeuroChannel/ 🌐 ICO Facebook https://www.facebook.com/BiNeuroPartnersEN/ 🌐 ICO Twitter https://twitter.com/BiNeuroPartners/ For Donations ✅📈💰 ETH: 0x4be0D62Fc296136326F1A6BB6E3A543472c41820 ✅📈💰 BITCOIN: 12BADp2mURAo3KS3aV72G4q38Ds18hPoG9 ✅📈💰 WAVES: 3PKA32nC4n7gXSa78hdcq5bjY1g1RTbSTkr ✅📈💰 LITECOIN: LatVwVHfD9UF3msXsvLkkWXxD9j3gV8tbQ 👉👉👉👉👉Author Info: 🎁💰 My Bitcointalk : https://bitcointalk.org/index.php?action=profile;u=1843240 🎁💰 BountyHive: http://bountyhive.io/r/ejof 🎁💰 My Telegram : https://t.me/kholiq321 🎁💰 My Twitter : https://twitter.com/lilaelek 🎁💰 My Email : [email protected] DISCLAIMER: This is NOT financial advice. This is just my opinions.
Views: 1757 ICO REVIEW
System Identification: Dynamic Mode Decomposition with Control
This lecture provides an overview of dynamic mode decomposition with control (DMDc) for full-state system identification. DMDc is a least-squares regression technique based on the singular value decomposition (SVD). Dynamic mode decomposition with control J. L. Proctor, S. L. Brunton, and J. N. Kutz, SIAM Journal on Applied Dynamical Systems, 15(1):142–161, 2016. https://epubs.siam.org/doi/abs/10.1137/15M1013857 https://arxiv.org/abs/1409.6358 https://www.eigensteve.com/ http://dmdbook.com/
Views: 1701 Steve Brunton
Numerical weather prediction
Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs. Mathematical models based on the same physical principles can be used to generate either short-term weather forecasts or longer-term climate predictions; the latter are widely applied for understanding and projecting climate change. The improvements made to regional models have allowed for significant improvements in tropical cyclone track and air quality forecasts; however, atmospheric models perform poorly at handling processes that occur in a relatively constricted area, such as wildfires. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 3298 Audiopedia
Principal Component Analysis (PCA) using Microsoft Excel video
Learn how to visualize the relationships between variables and the similarities between observations using Analyse-it for Microsoft Excel. The tutorial covers the following tasks: - Understanding the relationship between variables - Reducing the dimensionality of the data - Understanding the similarities between observations For more information and to download the tutorial examples, visit http://analyse-it.com/docs/tutorials/correlation/overview
Views: 76264 Analyse-it
Dynamic Mode Decomposition (Examples)
In this video, we continue to explore the dynamic mode decomposition (DMD). In particular, we look at recent methodological extensions and application areas in fluid dynamics, disease modeling, neuroscience, and multiscale physics. http://dmdbook.com/ https://www.eigensteve.com/
Views: 2884 Steve Brunton
stock returns regression in excel
Download excel file to go with video: http://www.codible.com/pages/84 Analyze stock price data using Microsoft Excel to plot returns, and plot a regression line between the stock returns. Some good books on Excel and Finance: Financial Modeling - by Benninga: http://amzn.to/2tByGQ2 Principles of Finance with Excel - by Benninga: http://amzn.to/2uaCyo6
Views: 88001 Codible
Pixelation and the spectrum of singular values
An image matrix is gradually pixelated and changes in the spectrum of singular values are displayed.
Views: 55 Daniel M. Topa
Mod-03 Lec-18 Multivariate Analysis - III
Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit http://nptel.ac.in
Views: 612 nptelhrd
Ilia Binder (Toronto) • Multifractal Spectrum of SLE Boundary Collisions
Abstract: I will discuss the multifractal spectrum of the intersection of chordal SLEκ curves with the real line. For κ greater than 4, this intersection is a random fractal of almost sure Hausdorff dimension min 2−8/κ,1. We study the random sets of points at which the curve collides with the real line at a specified angle - or, equivalently, the local dimension of harmonic measure is prescribed - and compute an almost sure dimension spectrum describing the metric size of these sets. The talk is based on a joint work with Tom Alberts, Utah and Fredrik Viklund, KTH. Conference: 2016 CMS Winter Meeting Session: Fractal Geometry, Analysis, and Applications Link: https://cms.math.ca/Events/winter16/sessions_scientific#fg Webpage: http://jzsfvss.blogspot.com/2016/12/fractal-geometry-analysis-and.html
Views: 128 József Vass
Machine Learning 4 - Clustering
Using K-Means algorithm to cluster stocks data to find similarity between different stocks. Covering how to retrieve stock prices from Yahoo! Finance using IPython Notebook and matplotlib.finance. Also finding the best number of clusters according to measuring multiple scores like error and failed clusters. Open Source Notebook Viewer: http://nbviewer.ipython.org/github/TwistedHardware/mltutorial/blob/master/notebooks/Lesson%204%20-%20Clustering.ipynb For question please leave a comment on this video or use my social media: https://twitter.com/twistedhardware https://plus.google.com/+Tcsaroshan/ Links used in the video: Amazon AWS: http://goo.gl/RIeSjK/ Roshan Project: http://goo.gl/oFmMc1/
Views: 5418 Roshan
Removing foreground elements via dynamic mode and singular value decompositions
Using dynamic mode and singular value decompositions, we can remove the foreground elements of an image.
Views: 275 From Aphony
12.  Time series and visual decomposition
Lecture on: - time series (CO2 mainly) - seasonal trend decomposition(s) - scatterplot smoothing via locally weighted sum of squares (LoWeSS) - decomposing a scatterplot into a Large span smoother + low span smoother (+ residuals)
Views: 314 Wayne Oldford
Pierre Del Moral, INRIA (Bordeaux-Sud Ouest Research Center) Ensemble Kalman-Bucy filters
London Probability Seminar 4/11/2016 https://nms.kcl.ac.uk/probability/events.php?pastFuture=past Pierre Del Moral, INRIA (Bordeaux-Sud Ouest Research Center) On the stability and the uniform propagation of chaos properties of Ensemble Kalman-Bucy filters The Ensemble Kalman filter is a sophisticated and powerful data assimilation method for filtering high dimensional problems arising in fluid mechanics and geophysical sciences. This Monte Carlo method can be interpreted as a mean-field McKean-Vlasov type particle interpretation of the Kalman-Bucy diffusions. In contrast to more conventional particle filters and nonlinear Markov processes these models are designed in terms of a diffusion process with a diffusion matrix that depends on particle covariance matrices. Besides some recent advances on the stability of nonlinear Langevin type diffusions with drift interactions, the long-time behaviour of models with interacting diffusion matrices and conditional distribution interaction functions has never been discussed in the literature. One of the main contributions of the article is to initiate the study of this new class of models The article presents a series of new functional inequalities to quantify the stability of these nonlinear diffusion processes. In the same vein, despite some recent contributions on the convergence of the Ensemble Kalman filter when the number of sample tends to infinity very little is known on stability and the long-time behaviour of these mean-field interacting type particle filters. The second contribution of this article is to provide uniform propagation of chaos properties as well as Lp-mean error estimates w.r.t. to the time horizon. Our regularity condition is also shown to be sufficient and necessary for the uniform convergence of the Ensemble Kalman filter. The stochastic analysis developed in this article is based on an original combination of functional inequalities and Foster-Lyapunov techniques with coupling, martingale techniques, random matrices and spectral analysis theory. This is joint work with Julian Tugaut.
Views: 230 KCL Probability
SSA confidence intervals
Shows how to put 95% Confidence bands around a SSA (caterpillar method) smoothed reconstruction of price. The smoothed reconstuction is produce using XLCycles and the confidence intervals are calculated using standard Microsoft Excel techniques.
Views: 3195 XLTraderSoftware
Introduction to the dynamic clamp hardware and setup.
This short movie introduces the main hardware components used in a dynamic clamp experiment with StdpC. Thomas Nowotny and Michael Crossley introduce the hardware (electrophysiology rig, amplifier, data acquisition, dynamic clamp data acquisition board), the Lymnaea preparation, and an example of a pattern clamp experiment. From: Kemenes, I. et al. "Dynamic clamp with StdpC software." Nature Protocols 6, 405--417 (2011) doi:10.1038/nprot.2010.200 http://dx.doi.org/10.1038/nprot.2010.200
Views: 1354 ProtocolExchange
XL4Sim Beam Harmonic Response Analysis Example #1
XL4Sim MS Excel COM Addin for harmonic response beam analysis. Compatible with with MS Office Versions 2000 through 2013. Complex beam configurations can be evaluated very quickly. Multiple beam segments with varying section properties can be modeled. Various boundary conditions and loads can be applied. Results include deflection, moment, shear and stress diagrams. The deflection diagram can be animated. The Excel COM Add-in can be found at XL4Sim.com or Spreadsheets4Engineers.com.
Views: 158 James Pike
Growth of (finite) Perturbations in Spatiotemporal Systems. Application to Ensemble Weather Fore...
- By: José Manuel Gutiérrez Llorente, Instituto de Física de Cantabria - IFCA (CSIC-UC), Santander - Date: 2012-05-23 14:30:00 - Description: Modern meteorology is concerned with the nonlinear dynamics of the atmosphere and ocean (ensemble prediction, etc.). However, although low dimensional chaos is nowadays well known, much less is understood about spatiotemporal systems with a large number of degrees of freedom, as those describing the atmosphere-ocean evolution. The nonlinear effects that drive the dynamics of finite perturbations are a key factor for the understanding of error growth and ensemble prediction in these systems. However, most of the methods used currently in meteorology rely on findings from the low dimensional world and do not take into account the interactions between space and time inherent of these systems. In this talk we focus on the singular features of spatiotemporal chaos, as opposed to low-dimensional dynamics, paying attention to the interplay between spatial and temporal dynamics. To this aim we consider not only infinitesimal dynamics (characterized by the Lyapunov spectrum), but also the properties of finite growth in the edge of predictability. A recent analogy introduced with the scaling (fractal) growth of rough interfaces (such as the propagation of a fire interface in a sheet of paper) provides a framework for the analysis and characterization of the spatial and temporal dynamics of these systems, and their interplay. As an illustrative example we analyze data from the DEMETER project, an state-of-the-art ensemble forecast system for seasonal forecasting, which includes multiple models and initial conditions. More details in http://www.meteo.unican.es/en/research/spatiotemporal_chaos Herrera, S., Pazó, D., Fernández, J., Rodríguez, M.A., (2012) Tellus A. In press. Herrera, S., Fernández, J., Rodríguez, M. A. and Gutiérrez, J.M., (2010) Nonlin. Processes Geophys. 17, 329–337. Fernández, J., Primo, C., Cofiño, A.S., Gutiérrez, J.M., Rodríguez, M.A., (2009) Climate Dynamics, 33, 233-243.
Principal Component Analysis (PCA) using Python (Scikit-learn)
Principal Component Analysis (PCA) using Python (Scikit-learn) Step by Step Tutorial: https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60
Views: 52487 Michael Galarnyk
SoMAS/ITPA - Comparing Low-frequency and Intermittent Variability in Climate Models
Dimitris Giannakis from the Center for Atmospheric Ocean Science at New York University speaks at SoMAS on Wednesday, November 14, 2012. "Comparing Low-frequency and Intermittent Variability in Climate Models Through Nonlinear Laplacian Spectral Analysis"
Views: 120 SoMAS SBU
October 2018; Energy Cloud Event
I just experienced a visible cloud of energy, which moved down the river I was next to. It was singular, with no other "clouds" or "must" around. It was "raining" energy inside the cloud itself, which I felt as it passed through me. So I'm wondering if anyone else has experienced anything like this, or any other unusual events recently? I and I'm sure many more, would love to hear your experiences if you'd care to share them in the comments below. The more we share with eachother, the more were all informed! 🙂 xx Website: https://pipercheyanne.com Facebook: https://www.facebook.com/PiperCheyanneIntuitive/ Instagram: https://www.instagram.com/pipercheyanne I'm Piper Cheyanne and like some people have the ability to see more of the light spectrum than others, I have a similar ability in that I have an expanded awareness that allows me to access Universal energies and information that others aren't consciously aware of. As an intuitive empath and a physical channel, I feel and see energy very intensely. I use the expanded information that I access, along with cutting edge quantum information, to help us paint a clearer picture of what's currently happening to humanity and our planet. 🌈🌎🌈 I work with people both one-to-one and in groups, to assist them in accessing the abilities of their true inner being. In this way, I kind of hold the bicycle seat for others, accessing the energies, so that they can access their own magic and miracles in the spiritual world. I find that this helps people step into their own life's purpose and understand the larger story of what's currently taking place on the planet today. I'm also a "Quantum Healing Hypnosis Technique" ( QHHT) practitioner, which is a deep hypnosis technique that allows you to speak to your Higher Self, clear life long problems and access life changing information. If you'd like to find out more about me, or to book a session, please visit my website listed below. Also, please feel free to Like, Subscribe and Share this video if you think anyone else may benefit from it. Thank you so much for watching! If you'd like to get in touch with me, you can find me at: Website: https://pipercheyanne.com Facebook: https://www.facebook.com/PiperCheyanneIntuitive/ Instagram: https://www.instagram.com/pipercheyanne
Views: 94 Piper Cheyanne
Lecture - 38 Autoregressive Modeling and Linear Prediction
Lecture Series on Probability and Random Variables by Prof. M.Chakraborty, Dept. of Electronics and Electrical Engineering,I.I.T.,Kharagpur.For more Courses visit http://nptel.iitm.ac.in
Views: 25998 nptelhrd
Mod-08 Lec-32 Linear Stochastic Dynamics - Kalman Filter
Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 2369 nptelhrd
Randomized Low-Rank Approximation and PCA: Beyond Sketching, Cameron Musco
I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximations offered by random sketching algorithms. In particular, we will see how advances in Johnson-Lindenstrauss projection methods have provided tools for improving the analysis of classic iterative SVD algorithms, including the block power method and block Krylov methods. The key insight is to view the iterative algorithms as denoising procedures for coarse sketching methods. I will discuss how this view can be used to analyze a simple block Krylov method, showing that the algorithm gives (1+epsilon) near optimal PCA and low-rank approximation in just O(1/sqrtepsilon) iterations. Despite their long history, this analysis is the first of a Krylov subspace method that does not depend on the matrixs spectral gaps. I will also survey open questions in the analysis of iterative methods, promising work on approximate PCA via stochastic optimization, fast sampling methods for low-rank kernel matrix approximation, and faster techniques for singular value decomposition targeted at specific downstream tasks, such as principal component regression.
Views: 1647 MMDS Foundation