'Time series data' presentation slideshows

Time Series Analysis: Method and Substance Introductory Workshop on Time Series Analysis

Time Series Analysis: Method and Substance Introductory Workshop on Time Series Analysis. Sara McLaughlin Mitchell Department of Political Science University of Iowa. Overview. What is time series? Properties of time series data Approaches to time series analysis

1.23k views • 79 slides

CHAPTER 1

CHAPTER 1. INTRODUCTION. WHAT IS STATISTICS?. Definition Statistics is a group of methods used to collect, analyze, present, and interpret data and to make decisions. TYPES OF STATISTICS. Definition

1.63k views • 36 slides

AGEC 622

AGEC 622. I am James Richardson I get to be your teacher for the rest of the semester Jing Yi will be the grader for this section. Brian Herbst will assist in the Labs If you have problems with homeworks see Jing, Brian, or me Are you attending the labs?

413 views • 30 slides

??????????? Content and Knowledge Management Laboratory (B)

??????????? Content and Knowledge Management Laboratory (B). Data Mining Part Director: Anthony J. T. Lee Presenter: Wan-chuen Lin. Outline. Introduction of basic data mining concepts about our research topics Brief description of doctoral research

365 views • 30 slides

Non-continuous Relationships

Non-continuous Relationships. If the relationship between the dependent variable and an independent variable is non-continuous a slope dummy variable can be used to estimate two sets of coefficients and intercepts for the independent variable.

437 views • 29 slides

Non-continuous Relationships

Non-continuous Relationships. If the relationship between the dependent variable and an independent variable is non-continuous a slope dummy variable can be used to estimate two sets of coefficients and intercepts for the independent variable.

430 views • 29 slides

Course is about Decision Making

Course is about Decision Making. How do you make decisions? Do you list all of the possible outcomes and their consequences? Do you think about the probabilities for each possible outcome? P(win) = 25% Do you fixate on the outcome you want? P(win ) = 100%

445 views • 27 slides

Multivariate Regression

Multivariate Regression . Topics . The form of the equation Assumptions Axis of evil (collinearity, heteroscedasticity and autocorrelation) Model miss-specification Missing a critical variable Including irrelevant variable (s) . The form of the equation. Y t = Dependent variable

641 views • 24 slides

Political Party & Unemployment

Political Party & Unemployment. Econ 240A Professor Llad Phillips. Group 5: Richard Baker, Michael Jelmini, Kenneth Morino, Cambria Price, You Ren, James Young. Why? Upcoming graduation Recent recession Recent increase in unemployment What?

560 views • 21 slides

Discovering Deformable Motifs in Time Series Data

Discovering Deformable Motifs in Time Series Data. Jin Chen CSE 891-001 2012 Fall. Background. Time series data are collected in multiple domains, including surveillance, pose tracking, ICU patient monitoring and finance.

345 views • 20 slides

MULTICOLLINEARITY

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Applied Econometrics 31 456

Applied Econometrics 31 456. This half of the course provides an introduction to stationary time series data. Nobel Prize for Economics in 2003 awarded to Rob Engle and Clive Granger, who highlighted the importance of stationarity in time series data.

728 views • 18 slides

Data and Statistics

380 views • 18 slides

Corps Water Management System (CWMS) Modernization

Corps Water Management System (CWMS) Modernization. Improved Real-Time Decision Support for Water Management 700+ Multipurpose Reservoirs and Flow Control Structures, Thousands of Miles of Levees Expanded Corporate Web-Based Information Standardized Corporate Hardware/Software Class IV AIS.

427 views • 16 slides

Hidden Process Models

Hidden Process Models. Rebecca Hutchinson Tom M. Mitchell Indrayana Rustandi October 4, 2006 Women in Machine Learning Workshop Carnegie Mellon University Computer Science Department. Introduction. Hidden Process Models (HPMs): A new probabilistic model for time series data.

298 views • 16 slides

AUTOCORRELATION (The violation of CLRM assumption)

AUTOCORRELATION (The violation of CLRM assumption). By RABIA GUL. CLASSICAL LINEAR REGRESSION MODEL. The general form is Y ? = ? + ? ? X ? + ??. Gauss Markov Theorem assumptions. The given population function is linear in parameters and is correctly specified

613 views • 15 slides

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CHAPTER 10.

CHAPTER 10. MULTICOLLINEARITY: WHAT HAPPENS IF THE REGRESSORS ARE CORRELATED?. Assumption 10 of the CLRM. No Multicollinearity among the regressors in the regression model. The questions in Multicollinearity: What is the nature of multicollinearity? Is multicollinearity really a problem?

437 views • 8 slides

Intro to Business Forecasting

Intro to Business Forecasting. Virtually all business decisions require decision-makers to form expectations about business/market conditions in the future. Hiring Purchase of raw materials, semi-finished and finished goods for inventories Plant/store closures

199 views • 6 slides