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Notes for Exam 2

Notes for Exam 2 - ISDS 2001 Notes for Exam 2 Chapter 18...

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ISDS 2001 Notes for Exam 2 2/12/09 Chapter 18 Time Series A times series is a set of observations measured at successive points in time or over successive periods of time. Components of a Time Series o Trend Component The trend component accounts for the gradual shifting of the time series to relatively higher or lower values over a long period of time Trend is usually the result of long term factors such as changes in the population, demographic characteristics of the population, technology, and/or consumer preferences o Cyclical Component Any regular pattern of sequences of values above and below the trend line lasting more than one year can be attributed to the cyclical component Usually, this component is due to multiyear cyclical moments in the economy. o Seasonal Component The seasonal component accounts for regular patterns of variability within certain time periods, such as a year. The variability does not always correspond with the seasons of the year (i.e. winter, spring, summer, fall). There can be, for example, within-week or within-day “seasonal behavior. o Irregular Component The irregular component is caused by short-term, unanticipated and non-recurring factors that affect the values of the time series. This component is the residual, or “catch-all”, factor that accounts for unanticipated data values It is unpredictable. 3/3/09 Smoothing Methods In cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular component of the time series. Three components of smoothing methods are: o Moving Averages o Weighted Moving Averages o Exponential Smoothing Moving Averages The moving averages method consists of computing an average of the most recent n data values for the series and using this average for forecasting the value of the time series for the next period. MSE (Mean Square Error) – an often used measure of accuracy of a forecasting method Weighted Moving Averages To use this method we must first select the number of data values to be included in the average. Next we must choose the weight for each of the data values.
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The more recent observations are typically given more weight than older observations For convenience, the weights usually sum to 1. Exponential Smoothing This method is a special case of a weighted moving averages method; we select only the weight for the most recent observation. The weight for the other data values are computed automatically and become smaller as the observations grow older. The exponential smoothing forecast is weighted average of all the observations in the time series 3/5/09 Business Analyst Elicit requirements using o Interviews o Surveys o Site visits o Task and work flow analysis Critically evaluate information gathered from multiple sources, reconcile conflicts, decompose high-level
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Notes for Exam 2 - ISDS 2001 Notes for Exam 2 Chapter 18...

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