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 ANALYSIS Introduction Economic and business time series analysis is a major field of research and application. This analysis method has been used for economic forecasting‚ sales forecasting‚ stock market analysis and company internal control. In this paper‚ we will talk about time series and review techniques that are useful for analyzing time series data. Definition of Time Series and Time Series Analysis Time series is an ordered sequence of values of a variable at equally spaced
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TIME SERIES ANALYSIS Chapter Three Univariate Time Series Models Chapter Three Univariate time series models c WISE 1 3.1 Preliminaries We denote the univariate time series of interest as yt. • yt is observed for t = 1‚ 2‚ . . . ‚ T ; • y0‚ y−1‚ . . . ‚ y1−p are available; • Ωt−1 the history or information set at time t − 1. Call such a sequence of random variables a time series. Chapter Three Univariate time series models c WISE 2 Martingales Let {yt} denote
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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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Competition environment 4.9.2 How –simulation method There are many ways to create a forecast for a certain goods that a planner can use just one or combine many methods together. According to Chopra and Meindl define these methods as follows: 1. Qualitative methods Rely upon a person’s intuition or subjective opinions about a market. These methods are most appropriate when there is not much historical data to work with. 2. Causal methods assume that demand is strongly related to a particular
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options: Put options: a) Based on the current stock price‚ which one of the two options is in the money? by how much? (1 marks) b) Assume an investor would like to gain exposure to 1000 shares of Google in his equity portfolio. The company’s earnings will be released on January 7th‚ after the market closes‚ and the investor believes the earnings will be very positive. Using the information provided above‚ outline two trading strategies for this investor and how much will it cost to execute each
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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 to determine
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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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.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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