'Stationarity' diaporamas de présentation

Time series analysis

Time series analysis. Example. Objectives of time series analysis. Classical decomposition: An example. Transformed data. Trend. Residuals. Trend and seasonal variation. Objectives of time series analysis. Unemployment data. Trend. Trend plus seasonal variation.

800 views • 56 slides

Random variables

Random variables. Random variables are extensively used in communication circuits Let the random variable represents the functional relationship between a random variable A and a real number For simplicity the rand variable will be denoted by X

637 views • 43 slides

An Introduction to Time Series

An Introduction to Time Series. Ginger Davis VIGRE Computational Finance Seminar Rice University November 26, 2003. What is a Time Series?. Time Series Collection of observations indexed by the date of each observation Lag Operator Represented by the symbol L Mean of Y t = ? t.

1.07k views • 34 slides

Time Series

Time Series . Instructor Dr. L. D. Behera Department of Electrical Engineering Indian institute of Technology Kanpur. Presented by Vikas Kumar vidyarthi Ph.D Scholar (10203069),CE. Contents:-. Correlation and Regression What is Time Series? Field of its Applications Methods:

506 views • 30 slides

Queuing Models

Queuing Models. Basic Concepts. QUEUING MODELS. Queuing is the analysis of waiting lines It can be used to: Determine the # checkout stands to have open at a store Determine the type of line to have at a bank Determine the seating procedures at a restaurant

673 views • 23 slides

Fluid equations, magnetohydrodynamics

866 views • 21 slides

Econometrics 1

Econometrics 1. Lecture 11 Stationarity and AR MA and ARMA Processes. Stationary and Nonstationary Series. Spurious Regression. Four ways of checking stationarity. Partial autocorrelation function and Ljung and Box statistics. Unit Root and Non-stationarity. Time Dependent Variance.

547 views • 20 slides

Univariate analysis: Analysis of a the properties of a X and Y

Univariate analysis: Analysis of a the properties of a X and Y Bivariate analysis: Analysis of the relationship between X and Y. Definition of time series: a series of succesive observation over time; e.g. daily, monthly, yearly Output series: Y t Input series: X t

401 views • 20 slides

MULTIVARIATE TIME SERIES & FORECASTING

MULTIVARIATE TIME SERIES & FORECASTING. : autocovariance function of the individual time series . Vector ARMA models. Stationarity. if the roots of the equation are all greater than 1 in absolute value . Then : infinite MA representation. Invertibility.

418 views • 18 slides

Perspectives in handling uncertainty in European data

Perspectives in handling uncertainty in European data. Philippe Crouzet European Environment Agency (EEA). Contents. Importance of uncertainty Uncertainty in the assessment process Uncertainty in the Policy questions proper Two open questions, among others Gross nutrient balances

247 views • 13 slides

Based on otolith microchemistry

Based on otolith microchemistry. Spawning stocks in fishing areas. Based on otolith microchemistry. Nursery grounds supplying spawning locations. the populations e.g. 1) How many populations? 2) Spatial and temporal distributions 3) Mixing hypothesis, e.g. diffusion/overlap

409 views • 12 slides

Nonstationarities in teletraffic data which may spoil your statistical tests

Nonstationarities in teletraffic data which may spoil your statistical tests. Piotr ?uraniewski (UvA/TNO/AGH) Felipe Mata (UAM), Michel Mandjes (UvA), Marco Mellia (POLITO). Stationarity. Many models assume stationarity: statistical properties do not change over time

196 views • 10 slides