'Ma 1' diaporamas de présentation

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Ka-fu Wong University of Hong Kong

Ka-fu Wong University of Hong Kong. Modeling Cycles: MA, AR and ARMA Models. Unobserved components model of time series. A ccording to the unobserved components model of a time series, the series y t has three components y t = T t + S t + C t. Cyclical component. Time trend.

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STAT 497 LECTURE NOTES 7

STAT 497 LECTURE NOTES 7. FORECASTING. FORECASTING. One of the most important objectives in time series analysis is to forecast its future values. It is the primary objective of modeling. ESTIMATION (tahmin) ?the value of an estimator for a parameter.

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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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The Box-Jenkins (ARIMA) Methodology

The Box-Jenkins (ARIMA) Methodology. ARIMA Models ( time series modeling ). A uto R egressive I ntegrated M oving A verage (ARIMA) models model stationary as well as nonstationary data do not involve independent variables in their construction

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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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ColdFusion Basics

ColdFusion Basics. A goal properly set is halfway reached. Zig Ziggler. Perspective. Mystery Guest #1 Mystery Guest #2 Mystery Guest #3. The Evolution of a Developer. Different Approaches. AJAX (Browser). AIR (Desktop w/Flex). Usability. Tags. Script. < cfscript >

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Modeling of Multi-resolution Active Network Measurement Time-series

Modeling of Multi-resolution Active Network Measurement Time-series. Prasad Calyam, Ph.D. Ananth Devulapalli pcalyam@osc.edu ananth@osc.edu. Third IEEE Workshop on Network Measurements October 14 th 2008. Topics of Discussion. Background. Measurement Data Sets.

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Time Series Analysis

Time Series Analysis. PART II. Econometric Forecasting

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AR- MA- and ARMA-

AR- MA- and ARMA-. How should these data be modelled?. Identification step: Look at the SAC and SPAC. Looks like an AR (1)-process. (Spikes are clearly decreasing in SAC and there is maybe only one sign. spike in SPAC). Then we should try to fit the model

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AR- MA- och ARMA-

AR- MA- och ARMA-. How should these data be modelled?. Identification step: Look at the SAC and SPAC. Looks like an AR (1)-process. (Spikes are clearly decreasing in SAC and there is maybe only one sign. spike in SPAC). Then we should try to fit the model

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-------Quasielastic Scattering in MINERvA -------

-------Quasielastic Scattering in MINERvA ------- A. Bodek, H. Budd - Rochester will get Steve Manly -Rochester and Tony Man -Tufts also involved. Clear example of Bridging Physics between Jlab and MINERva at Fermilab Physics:

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High Energy Electron Acceleration Using Plasmas, 6-10 June , Paris, 2005

Laser Electron Acceleration Project at JAERI Masaki Kando Advanced Photon Research Center Japan Atomic Energy Research Institute (JAERI). High Energy Electron Acceleration Using Plasmas, 6-10 June , Paris, 2005. Collaborators.

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SYSTEMS Identification

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Teknik Peramalan: Materi minggu kedelapan

Teknik Peramalan: Materi minggu kedelapan. ? Model ARIMA Box-Jenkins ? Identification of STATIONER TIME SERIES ? Estimation of ARIMA model ? Diagnostic Check of ARIMA model ? Forecasting

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Density

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???? ?????????????????????????????????????????????????. ???????. ???????. F. V, s,t. ???????. ???????????????????????????????????. ? F = 0 ? ?????????. ?. ? F ?0 ? ??????? a ?0. ?. ? F= ma. ?????. a. ????????????????????? ??????????????. F. N.

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SAP HANA Interview Questions

SAP HANA is a mammoth module

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% cell viability

HCC827ER HGF(-). HCC827ER HGF(+). % cell viability. % cell viability. Drug concentration (?M). Drug concentration (?M). Supplementary Figure 1. The effect of 17-DMAG on the growth of lung cancer cells with Met amplification

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