report on the time-series analysis of continuously compounded returns for Ford and GM for the periods January 2002 till April 2007 using monthly stock prices. This analysis is aimed at estimating the ARIMA model that provides the best forecast for the series. This paper will be divided into 2 sections; the first section showing the Ford analysis and the second the GM analysis. Section 1: Ford Figure 1: Time series plot for raw Ford data. Figure 1 shows a time series plot of the
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Secondary Research Time Series Analysis VARIABLE FACTOR THAT INCREASING MALAYSIA GDP Prepared by: Dina Maya Avinati Wery Astuti Faculty of Business UNIVERSITAS SISWA BANGSA INTERNATIONAL Mulia Business Park‚ JL. MT. Haryono Kav. 58-60 Pancoran- South Jakarta Page | 1 CONTENT I. Introduction 1.1 Back Ground of Study 1.2 Problem 1.3 Research Problem 1.4 Research Objective 1.5 Scope and Limitation 1.6 Significant of Study II. Literature Review
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Course Outline for Spring 2012‚ Statistics 153: Introduction to Time Series January 16‚ 2012 • Instructor: Aditya Guntuboyina (aditya@stat.berkeley.edu) • Lectures: 12:30 pm to 2 pm on Tuesdays and Thursdays at 160 Dwinelle Hall. • Office Hours: 10 am to 11 am on Tuesdays and Thursdays at 423 Evans Hall. • GSI: Brianna Heggeseth (bhirst@stat.berkeley.edu) • GSI Lab Section: 10 am to 12 pm OR 12 pm to 2 pm on Fridays at 334 Evans Hall (The first section will include a short Introduction
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Time Series Regression 3.1 A small regional trucking company has experienced steady growth. Use time series regression to forecast capital needs for the next 2 years. The company’s recent capital needs have been: ══════════════════════════════════════════════ Capital Needs Capital Needs (Thousands Of (Thousands Of Year Dollars) Year Dollars) -------------------------------------------
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EXAMINATION PAPER (DUE ON DECEMBER 11‚ 2013) You are expected to write a small individual paper as an examination requirement. The research paper is your individual forecasting project with actual economic and business data. The maximum size of the paper is 15 A4 pages. Before choosing the topic for your forecasting project‚ think carefully what your interests are‚ and what kind of information you would like to explore. Once you understand your interests‚ search for the relevant data AND
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E cient neighbor searching in nonlinear time series analysis Thomas Schreiber Department of Theoretical Physics‚ University of Wuppertal‚ D{42097 Wuppertal July 18‚ 1996 We want to encourage the use of fast algorithms to nd nearest neighbors in k{dimensional space. We review methods which are particularly useful for the study of time series data from chaotic systems. As an example‚ a simple box{assisted method and possible re nements are described in some detail. The e ciency of the method is compared
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Time Series Prediction of Earthquake Input by using Soft Computing Hitoshi FURUTA‚ Yasutoshi NOMURA Department of Informatics‚ Kansai University‚ Takatsuki‚ Osaka569-1095‚ Japan nomura@sc.kutc.kansai-u.ac.jp Abstract Time series analysis is one of important issues in science‚ engineering‚ and so on. Up to the present statistical methods[1] such as AR model[2] and Kalman filter[3] have been successfully applied‚ however‚ those statistical methods may have problems for solving highly nonlinear
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.2.3 Time series models Time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series occur frequently when looking at industrial data. The essential difference between modeling data via time series methods and the other methods is that Time series analysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation‚ trend or seasonal variation that should be accounted for. A Time-series model explains
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Regression with Time Series Data Week 10 Main features of Time series Data Observations have temporal ordering Variables may have serial correlation‚ trends and seasonality Time series data are not a random sample because the observations in time series are collected from the same objects at different points in time For time series data‚ because MLR2 does not hold‚ the inference tools are valid under a set of strong assumptions (TS1-6) for finite samples While TS3-6 are often too restrictive
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Business Statistics I: QM 1 Lecture N otes by Stefan W aner (5th printing: 2003) Department of Mathematics‚ Hofstra University BUSINESS STATISTCS I: QM 001 (5th printing: 2003) LECTURE NOTES BY STEFAN WANER TABLE OF CONTENTS 0. Introduction................................................................................................... 2 1. Describing Data Graphically ...................................................................... 3 2. Measures of Central Tendency
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