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
Premium Time series analysis Time Term
FORECASTING Managers are always trying to reduce uncertainty and make better estimates of what will happen in the future; this is the main purpose of forecasting. Some firms use subjective methods‚ seat-of-the pants methods‚ intuition‚ and experience. There are also several quantitative techniques‚ moving averages‚ exponential smoothing‚ trend projections‚ and least squares regression analysis. Eight steps to forecasting: * Determine the use of the forecast—what objective are we trying to
Premium Optimization Inventory Operations research
Six Rules of Effective Forecasting Q1: Write a summary about the six rules of effective forecasting? Paul Saffo is the author of the article of six rules for effective forecasting. He points out that effective forecasting is very different from accurate forecasting as it is possible that a forecast is effective but it may or may not be accurate. Accurate forecasting entails being unsure of the situation and one should not race to answers. Effective forecasting on the other hand means looking at
Premium Future Forecasting Prediction
Appropriate Forecasting Model Forecasting is done by monitoring changes that occur over time and projecting into the future. Forecasting is commonly used in both the for-profit and not-for-profit sectors of the economy. There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are especially important when historical data are unavailable. Qualitative forecasting methods are considered to be highly subjective and judgmental. Quantitative forecasting methods
Premium Regression analysis Statistics Errors and residuals in statistics
GAC013 Assessment Event2: Case Study Investigation Compare and Contrast Tsunamis and Volcanic Eruption Forecasting Student’s Name: Sissy Wang Student ID#: SHSA16374 Teacher: Kenny Due Date: 14th December 2011 Word Count: 1‚194 Table of Contents 1. Abstract Page 2 2. Introduction Page 2 3. Methodology Page 4 4. Finding Page 4 5. Discussion Page 6 6. Conclusions and Recommendations Page 6 7. Reference Page 7 Abstract With the development of science
Free Volcano Earthquake
ScienceAsia 27 (2001) : 271-278 Demand Forecasting and Production Planning for Highly Seasonal Demand Situations: Case Study of a Pressure Container Factory Pisal Yenradeea‚*‚ Anulark Pinnoib and Amnaj Charoenthavornyingb a Industrial Engineering Program‚ Sirindhorn International Institute of Technology‚ Thammasat University‚ Patumtani 12121‚ Thailand. b Industrial Systems Engineering Program‚ School of Advanced Technologies‚ Asian Institute of Technology‚ P.O. Box 4‚ Klong Luang‚ Patumtani
Premium Forecasting Planning Inventory
Answering the questions on the text: "Hard Rock Cafe - Forecasting" 1. Describe three different forecasting applications at Hard Rock. Name three other areas in which you think Hard Rock could use forecasting models. Hard rock café divide the forecast in long term methods where the expectations are to establish a better capacity plan and short term methods where they look for good contracts with suppliers for leather goods (clothes etc.) and definately to be more negotiable with the suppliers
Premium Term Prediction Time
Summary of Forecasting Profitability and Earnings In the competitive environment‚ there is a strong prediction in economic theory that profitability is mean reversion both within and across industries. For instance‚ under competition‚ firms will leave relatively profitless industries and turn into relatively high profitable industries. Some companies introduce new products and technologies that bring more profitability for an entrepreneur. Otherwise‚ the expectation of failure which makes companies
Premium Arithmetic mean Regression analysis Errors and residuals in statistics
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
Premium Time series Forecasting Regression analysis
Tornado forecasting can date back to 1948 where the first forecast was made by Capt. Robert C. Miller and Maj. Ernest J. Fawbush (Coleman‚ 567). This forecast was significant because of the Tinker Air Force Base tornadoes. Over a 5-day period in March of 1948‚ two tornadoes hit the base directly. They were the most destructive tornadoes to hit Oklahoma at that time. These two officers were able to pick up on the meteorological patterns and generate a forecast using a prognostic chart and weather
Premium Tropical cyclone Wind Hurricane Katrina