BUS 350 NOTE WEEK 9
BUS 350 NOTE WEEK 9 BUS 350
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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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