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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HTime series using Holt-Winters Forecasting Procedure Summary The Holt-Winters forecasting procedure is a simple widely used projection method which can cope with trend and seasonal variation. We can apply this method to lots of fields such as banking data analysis‚ investment forecasting‚ inventory controlling and so on. This paper shows us a practical banking credit card example using Holt-Winter method in Java programming for data forecasting. The reason we use Holt-Winter is that
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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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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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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 to the naive
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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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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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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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part you thoroughly motivate your interest in the time series you are about to analyze. You should argue why it is of interest and importance to model your data series. You also briefly report what you do in your project and what results and conclusions you reach. 3. Data. In this section you describe where and how you got the data. Carefully describe all data characteristics‚ length of your time series‚ and frequency. Make a graph of your data series; you could also make a table with summary statistics
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Chapter 08.02 Euler’s Method for Ordinary Differential Equations After reading this chapter‚ you should be able to: develop Euler’s Method for solving ordinary differential equations‚ determine how the step size affects the accuracy of a solution‚ derive Euler’s formula from Taylor series‚ and use Euler’s method to find approximate values of integrals. 1. 2. 3. 4. What is Euler’s method? Euler’s method is a numerical technique to solve ordinary differential equations of the form
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