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by: Dannyela

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# BUS 350 NOTE WEEK 9 BUS 350

Dannyela
Mercer
GPA 3.88

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This notes covers what's going to be on next final exam.
COURSE
Economic and Business Statistics
PROF.
Jun Liu
TYPE
Class Notes
PAGES
4
WORDS
CONCEPTS
Calculus Concepts
KARMA
Free

## Popular in Economic and Business Statistics

This 4 page Class Notes was uploaded by Dannyela on Wednesday July 27, 2016. The Class Notes belongs to BUS 350 at Mercer University taught by Jun Liu in Summer 2016. Since its upload, it has received 12 views. For similar materials see Economic and Business Statistics in Business Administration at Mercer University.

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Date Created: 07/27/16
Chapter 8 Time Series Analysis and Forecasting A time series is a sequence of observations on a variable measured at successive  points in time or over successive periods of time. • Forecasting methods can be classified as qualitative or quantitative  • Qualitative methods generally involve the use of expert judgment to develop  forecasts • Quantitative forecasting methods can be used when: • Past information about the variable being forecast is available • The information can be quantified • It is reasonable to assume that past is prologue 1. Horizontal Pattern A horizontal pattern exists when the data fluctuate randomly around a constant mean  over time.  A time series plot for a stationary time series will always exhibit a horizontal pattern with random fluctuations. The term stationary time series is used to denote a time series whose statistical  properties are independent of time.  2. Trend Pattern A trend pattern is gradual shifts or movements to relatively higher or lower values over a longer period of time. Although time series data generally exhibit random fluctuations, a time series may also  show gradual shifts or movements to relatively higher or lower values over a longer  period of time. A trend is usually the result of long­term factors such as population  increases or decreases, shifting demographic characteristics of the  population, improving technology, changes in the competitive landscape,  and/or changes in consumer preferences. A trend is usually the result of long­term factors such as: • Population increases or decreases • Shifting demographic characteristics of the population • Improving technology • Changes in the competitive landscape • Changes in consumer preferences 3. Seasonal Pattern  Are recognized by observing recurring patterns over successive periods of time. For example, a retailer who sells bathing suits expects low sales activity in the fall and winter months, with peak sales in the spring and summer months to occur every year. Retailers who sell snow removal equipment and heavy clothing, however, expect the opposite yearly pattern.  Seasonal patterns are recurring patterns over successive periods of time • Example: A manufacturer of swimming pools expects low  sales activity in the fall and winter months, with peak sales  in the spring and summer months to occur every year Time series plot not only exhibits a seasonal pattern over a one­year  period but also for less than one year in duration • Example: daily traffic volume shows within­the­day  “seasonal” behavior. 4. Trend and Seasonal Pattern • Some time series include both a trend and a seasonal pattern. 5. Cyclical Pattern A cyclical pattern exists if the time series plot shows an alternating sequence of points below and above the trend line that lasts for more than one year. Many economic time series exhibit cyclical behavior with regular runs of observations below and above the trend line. Often the cyclical component of a time series is due to multiyear business cycles. • Example: Periods of moderate inflation followed by periods  of rapid inflation can lead to a time series that alternates  below and above a generally increasing trend line Cyclical effects are often combined with long­term trend effects and  referred to as trend­cycle effects. 6. Identifying Time Series Patterns The underlying pattern in the time series is an important factor in selecting a forecasting method.  Thus, a time series plot should be one of the first analytic tools employed when trying to determine which forecasting method to use.  If we see a horizontal pattern, then we need to select a method appropriate for this type  of pattern.  Similarly, if we observe a trend in the data, then we need to use a forecasting method  that is capable of handling a trend effectively.

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