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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planning tool that helps management in its attempts to cope with the uncertainty of the future‚ relying mainly on data from the past and present and analysis of trends. Forecasting entails the use of historic data to determine the direction of future trends. Forecasting is used by companies to determine how to allocate their budgets for an upcoming period of time. This is typically based on demand for the goods and service it offers compared to the cost of producing them. Investors utilize forecasting
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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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TIME SERIES MODELS Time series analysis provides tools for selecting a model that can be used to forecast of future events. Time series models are based on the assumption that all information needed to generate a forecast is contained in the time series of data. The forecaster looks for patterns in the data and tries to obtain a forecast by projecting that pattern into the future. A forecasting method is a (numerical) procedure for generating a forecast. When such methods are not based upon
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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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Time Series behaviour of BOT in India: Evidence from Co integration Analysis and Error Correction Model xxxxxxxxxxxxxxxx Assistant Professor‚ Department of Business Administration‚ Xxxxxxxxxx West Bengal University of technology Kolkata‚ India Tel: +91-9231058348 E-mail: partha.s.sarkar@gmail.com Abstract India‚ a developing economy contains trade deficit from its very inception. The main objective of the study is to portray some characteristics of India’s trade in pre liberalization
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Time Series Analysis: The Multiplicative Decomposition Method Table of Contents Page Abstract………………………………………………………………………………………………………………………………………….3 Introduction………………………………………………………………………………………………………………………...…4-5 Methodology: Multiplicative Decomposition……………………………………………….…5-7 Advantages/Disadvantages of Multiplicative Method………………………………7-8 Conclusion…………………………………………………………………………………………………………………………………..8 Abstract One of the most essential pieces
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Forecasting Trends in Time Series Author(s): Everette S. Gardner‚ Jr. and Ed. McKenzie Reviewed work(s): Source: Management Science‚ Vol. 31‚ No. 10 (Oct.‚ 1985)‚ pp. 1237-1246 Published by: INFORMS Stable URL: http://www.jstor.org/stable/2631713 . Accessed: 20/12/2012 02:05 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use‚ available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars‚ researchers
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TIME SERIES AND FORECASTING McGrawHill/Irwin Copyright © 2010 by The McGrawHill Companies‚ Inc. All rights reserved. Time Series and its Components TIME SERIES is a collection of data recorded over a period of time (weekly‚ monthly‚ quarterly)‚ an analysis of history‚ that can be used by management to make current decisions and plans based on long-term forecasting. It usually assumes past pattern to continue into the future Components of a Time Series 1. 2. 3. 4. Secular Trend – the smooth
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