'Time series models' diaporamas de présentation

Dr. Jorge A. Prozzi The University of Texas at Austin Valparaiso, Chile, 10 November 2010

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Chapter 9. Analysis of Time Series Data

Chapter 9. Analysis of Time Series Data. 9.1 Basic concepts 9.2 General model formulations 9.3 Smoothing methods 9.4 OLS regression models 9.5 Stochastic time series models 9.6 ARIMAX or transfer function models 9.7 Quality control and process monitoring using control chart methods.

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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.

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Decomposition Method

Decomposition Method. Types of Data. Time series data: a sequence of observations measured over time (usually at equally spaced intervals, e.g., weekly, monthly and annually). Examples of time series data include: Gross Domestic Product each quarter; annual rainfall;

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Econ 240C

Econ 240C. Lecture Five. Part I: Time Series Components Model. The conceptual framework for inertial (mechanical) time series models: Time series (t) = trend + cycle + seasonal + residual We are familiar with trend models, e.g. Time series = a + b*t + e(t) , i.e.

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Classical Decomposition

Classical Decomposition. Boise State University By: Kurt Folke Spring 2003. Overview:. Time series models & classical decomposition Brainstorming exercise Classical decomposition explained Classical decomposition illustration Exercise Summary Bibliography & readings list

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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.

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Modeling the Evolution of Neurophysiological Signals

Modeling the Evolution of Neurophysiological Signals. Mark Fiecas Hernando Ombao. Data Characteristics. Small signal-to-noise ratios. Data Characteristics. Nonstationary time series data. Data Characteristics. Evolving over time within a replicate

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Time Series Model Estimation

Time Series Model Estimation. Materials for this lecture Read Chapter 15 pages 30 to 37 Lecture 6 Time Series.XLSX Lecture 6 Vector Autoregression.XLSX. Time Series Model Estimation. Outline for this lecture Review the first times series lecture Discuss model estimation

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Forecasting Demand

Forecasting Demand. Chapter 11. Forecasting Demand. Subjective Models Delphi Method Cross-Impact Historical Analogy Causal Models Regression Models Econometric Models Time Series Models N-Period Moving Average Simple Exponential Smoothing

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Forecasting Demand

Forecasting Demand. Chapter 11. Forecasting Demand. Subjective Models Delphi Method Cross-Impact Historical Analogy Causal Models Regression Models Econometric Models Time Series Models N-Period Moving Average Simple Exponential Smoothing

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Lecture 10 Time Series Model

Lecture 10 Time Series Model. Outline. Time series models Linear Time Series Models Moving Average Model Auto-regressive Model ARMA model Nonlinear Time Series Estimation Applications. What is a time series ? A scalar or vector-valued function of time indices Examples:

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Lecture 0

Lecture 0. Introduction. Course Information. Your instructor: Hyunseung (pronounced Hun-Sung) Or HK (not Hong Kong ?) E-mail : khyuns@wharton.upenn.edu Lecture: Time: Mon/Tues/Wed/ Thur at 10:45AM-12:15PM Location: F45 JMHH Office Hours: 434 JMHH

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Forecasting/ Causal Model

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