MODELS FOR STATIONARY TIME SERIES

Jul 28, 2014

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MODELS FOR STATIONARY TIME SERIES. Eni Sumarminingsih , SSi , MM. General Linear Processes. Moving Average Processes.

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general linear processes
average processes
stationary time series
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MODELS FOR STATIONARY TIME SERIES

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MODELS FOR STATIONARY TIME SERIES EniSumarminingsih, SSi, MM
General Linear Processes

Moving Average Processes

Stochastic models - time series.

Stochastic models - time series. Random process . an infinite collection of consistent distributions probabilities exist Random function . a family of random variables, e.g. {Y(t), t in Z}. Specified if given

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SOMs for time series

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Chapter 9 Regression with Time Series Data: Stationary Variables

9.1 Introduction9.2 Finite Distributed Lags9.3 Serial Correlation9.4 Other Tests for Serially Correlated Errors9.5 Estimation with Serially Correlated Errors9.6 Autoregressive Distributed Lag Models9.7 Forecasting9.8 Multiplier Analysis. Chapter Contents. 9.1 Introduction. When modeling rel

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Common Trend Models for Time Series

Common Trend Models for Time Series. Jee-Kwang Park Post-Doctoral Fellow in QuaSSI Department of Political Science Pennsylvania State University. Factor Analysis. The object of interest is not directly observable (measurable) But other seemingly related quantities are measurable

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Machine Learning Based Models for Time Series Prediction

Machine Learning Based Models for Time Series Prediction. 2014/3. Outline. Support Vector Regression Neural Network Adaptive Neuro -Fuzzy Inference System Comparison. Support Vector Regression. Basic Idea Given a dataset

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Chapter 9 Regression with Time Series Data: Stationary Variables

Chapter 9 Regression with Time Series Data: Stationary Variables. Walter R. Paczkowski Rutgers University. Chapter Contents. 9.1 Introduction 9.2 Finite Distributed Lags 9 .3 Serial Correlation 9 .4 Other Tests for Serially Correlated Errors

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MODELS FOR NONSTATIONARY TIME SERIES

MODELS FOR NONSTATIONARY TIME SERIES. By Eni Sumarminingsih , SSi , MM. Stationarity Through Differencing. Consider again the AR(1) model Consider in particular the equation. Iterating into the past as we have done before yields.

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Linear Stationary Processes. ARMA models

Linear Stationary Processes. ARMA models. This lecture introduces the basic linear models for stationary processes. Considering only stationary processes is very restrictive since most economic variables are non-stationary.

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TIME SERIES ANALYSIS Time Domain Models : Red Noise; AR and ARMA models

LECTURE 7. TIME SERIES ANALYSIS Time Domain Models : Red Noise; AR and ARMA models. Supplementary Readings : Wilks , chapters 8. Recall:. Statistical Model [ Observed Data ] = [ Signal ] + [ Noise ]. “ noise” has to satisfy certain properties!. If not, we must iterate on this process.

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Time-Series Forecast Models

Time-Series Forecast Models. A time series is based on a sequence of evenly time-spaced data points, such as daily shipments, weekly sales, or quarter- ly earnings. Forecasting time-series data implies that forecasts are predicted only from the past

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Change-Point Detection Techniques for Piecewise Locally Stationary Time Series

Change-Point Detection Techniques for Piecewise Locally Stationary Time Series. Michael Last National Institute of Statistical Sciences Talk for Midyear Anomaly Detection Workshop 2/3/2006. Stationary Time Series.

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S tatistical models for time series prediction

S tatistical models for time series prediction. Autoregressive model(AR(1)). y t =f(y t-1 ) y t = a1 *y t-1. y t = a0+a1 *y t-1 y t-1 = a0+a1 *y t-2 y t-2 = a0+a1 *y t-3 y t = a0 +a1 (a0 + a1(a0+a1*y t-3 )). y t-1. y t-2. Give expectation. White noise.

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ARIMA-models for non-stationary time series

ARIMA-models for non-stationary time series Consider again the data material from Exercise 8.8 in the textbook (weekly sales figures of thermostats). This series is obviously non-stationary as it possesses a trend. SAC and SPAC.

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Structural Break Detection in Time Series Models

Structural Break Detection in Time Series Models. Richard A. Davis Thomas Lee Gabriel Rodriguez-Yam Colorado State University (http://www.stat.colostate.edu/~rdavis/lectures) This research supported in part by: National Science Foundation IBM faculty award

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Stationary Time Series

Stationary Time Series. AMS 586. Let { X t | t  T } be defined by the equation. The Moving Average Time series of order q, MA(q). where { Z t | t  T } denote a white noise time series with variance s 2.

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Heavy Tails and Financial Time Series Models

Heavy Tails and Financial Time Series Models. Richard A. Davis Columbia University www.stat.columbia.edu/~rdavis Thomas Mikosch University of Copenhagen. Outline. Financial time series modeling General comments Characteristics of financial time series

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Time Series Models

Time Series Models. Stochastic processes Stationarity White noise Random walk Moving average processes Autoregressive processes More general processes. Topics. Stochastic Processes. 1. Time series are an example of a stochastic or random process

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Models for Non-Stationary Time Series

Models for Non-Stationary Time Series. The ARIMA( p,d,q ) time series. The ARIMA( p,d,q ) time series. Many non-stationary time series can be converted to a stationary time series by taking d th order differences. Let { x t |t  T } denote a time series such that

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Forecasting Exponential Smoothing For Stationary Models

Forecasting Exponential Smoothing For Stationary Models. Exponential Smoothing. The Last Period method uses only one period (the last) and the n-Period Moving Average and Weighted Moving methods use only the last n periods to make forecasts – the rest of the data is ignored .

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Stochastic models - time series.

Stochastic models - time series. Random process . an infinite collection of consistent distributions probabilities exist Random function . family of random variables, {Y(t;  ), t  Z,  } Z = {0,±1,±2,...},  a sample space. Specified if given

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Models for Non-Stationary Time Series

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