'Stationary time series' presentation slideshows

A uto r egressive I ntegrated M oving A verage Process ARIMA

A uto r egressive I ntegrated M oving A verage Process ARIMA. Model Building For ARIMA time series. Identification Estimation Diagnostic checking. ARIMA Model building. Identification Determination of p, d and q. To identify an ARIMA( p , d , q ) we use extensively

1.13k views • 88 slides

Linear Filters

Linear Filters. Let . denote a bivariate time series with zero mean. . Suppose that the time series { y t : t ? T } is constructed as follows:. The time series { y t : t ? T } is said to be constructed from { x t : t ? T } by means of a Linear Filter .

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Descriptive Statistics Introduction to Summary Statistics

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The Spectral Representation of Stationary Time Series

The Spectral Representation of Stationary Time Series. Stationary time series satisfy the properties: Constant mean ( E ( x t ) = m ) Constant variance (Var( x t ) = s 2 ) Correlation between two observations ( x t , x t + h ) dependent only on the distance h.

1.01k views • 65 slides

TIME SERIES: MODELLING

TIME SERIES: MODELLING. IDENTIFICATION. Stationary?. MANIPULATE. M O D E L I N G. DIAGNOSTICS. REVISED. F O R E C A S T. E V A L U A T E. Stages in Modeling. No. Yes. arma. arima. NOT GOOD. Assumptions.

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Forecasting

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Time Series Econometrics: Some Basic Concepts

Time Series Econometrics: Some Basic Concepts. Course Incharge : Prof. Dr. Himayatullah Khan. Reference : Gujarati, Chapters 21. Time Series Data. One of the important and frequent types of data used in empirical analysis.

3.85k views • 42 slides

Establish retention probabilities

Establish new student goal forecasts/ ranges. Establish retention probabilities. 1. 2. 3. 9. Monitor progress toward goals, adjust model and report progress. Undertake new student goal setting and what if analyses with operational areas. Projections Flow Model An Iterative Process. 4.

480 views • 35 slides

Adventures in high quantile estimation

Extreme Tail's Tales. Adventures in high quantile estimation. Joerg Rothenbuehler. The distribution of the Maximum:. Fisher-Tippett Theorem. The Extreme Value Distributions . Generalized Extreme Value Distribution (GEV).

423 views • 33 slides

The Relation between Drinks and Bread

The Relation between Drinks and Bread. Chia -chi Liu Stat, NCU 1/4/2011. Outline. Introduction and Motivation Data The Proposal Model Data Analysis Conclusions References. Introduction And Motivation. Nowadays, drinks have become indispensable in our life.

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The Effect of Long-memory Stochastic Volatility on the Anaysis of Confidence Interval for VaR Risk Measure

The Effect of Long-memory Stochastic Volatility on the Anaysis of Confidence Interval for VaR Risk Measure. Hwai-Chung Ho Institute of Statistical Science, Academia Sinica Sharon S. Yang Department of Business Mathematics, Soochow University Fang-I Liu

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Time Series Data

Time Series Data. y t = b 0 + b 1 x t1 + . . .+ b k x tk + u t 1. Basic Analysis. Time Series vs. Cross Sectional. Time series data has a temporal ordering, unlike cross-section data

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MODELS FOR STATIONARY TIME SERIES

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

486 views • 26 slides

Unit Roots & Forecasting

411 views • 22 slides

The Price Relationship Study of Stock

The Price Relationship Study of Stock. You- Sheng Liu 2011/01/04. Outline. 1. Motivation 2. Proposed method 3. Data analysis 4. Conclusions. Motivation & Introduction.

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Model ARIMA Box-Jenkins

Model ARIMA Box-Jenkins. Pertemuan 11. Metodologi Box-Jenkins:. ?. 1. Identifikasi model untuk sementara ? data lampau digunakan untuk mengidentifikasi model ARIMA yang sesuai. 2. Penaksiran parameter pada model sementara

1.44k views • 15 slides

Hierarchical Clustering of Time Series Data Streams

345 views • 13 slides

Stationary Stochastic Process

972 views • 11 slides

DATA STATIONER AND DATA NON STATIONER

DATA STATIONER AND DATA NON STATIONER. Example- Real GDP (2000 Prices) Seasonally Adjusted. (1) Plot Time Series - Non-Stationary (i.e. time varying mean and correlogram non-zero). GDP. Time. r. k. These are Examples of Non-Stationary Time Series. Unit Root Testing.

222 views • 11 slides