Lecture 5 S11

Lecture 5 S11 - Lecture 5 Forecasting Source/1 D.R.Anderson...

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Lecture 5 Forecasting Source: /1 / D.R.Anderson, D.J.Sweeney, T.A.Williams. Quantitative Methods for Business, South- Western College Publishing, 11-th edition. Chapter 6. Contents: 5.1 Components of a time series 5.2 Smoothing methods 5.3 Trend projection Planning for the future is an essential aspect of managing. But for good planning we should make good forecasts . How should we go about providing forecasts? In this Lecture we discuss two Time Series Methods : 1. Smoothing (moving averages, weighted moving averages, and exponential smoothing), 2. Trend projection , ________________________ Time series methods discover a pattern in the historical data (time series) and then extrapolate this pattern into the future. _________________________ 5.1 Components of a time series A time series is a set of observations of a variable measured at successive points in time or over successive periods of time. The pattern or behavior of the data in a time series has several components. The usual assumption is that four separate components— trend, cyclical, seasonal, and irregular —combine to provide specific values for the time series. Trend Component In time series analysis, the measurements may be taken every hour, day, week, month, or year, or at any other regular interval. Although time series data generally exhibit random fluctuations, the time series may still show gradual shifts or movements to relatively higher or lower values over a longer period of time. The gradual shifting of the time series over long period of time is referred to as the trend in the time series. This shifting or trend is usually the result of long- term factors such as changes in the population, demographic characteristics of the population, technology, and consumer preferences. For example , a manufacturer of photographic equipment may observe substantial month-to-month variability in the number of cameras sold. However, in reviewing sales over the past 10 to 15 years, this manufacturer may note a gradual increase in the annual sales volume. Suppose that the sales volume was approximately 1700 cameras per month in 1996, 2300 cameras per month in 2001, and 2500 cameras per month in 2006. Although actual month-to-month sales volumes may vary substantially, this gradual growth in sales shows an upward trend for the time series.
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Figure 5.1 shows a straight line that may be a good approximation of the trend in camera sales. Although the trend for camera sales appears to be linear and increasing over time, sometimes the trend in a time series can be described better by some other pattern. Linear trend of camera sales Figure 5.1. Figure 5.2 shows some other possible time series trend patterns. Part (a) shows a nonlinear trend; in this case, the time series shows little growth initially, then a period of rapid growth, and finally a leveling off. This trend pattern might be a good approximation of sales for a product from introduction through a growth period and into a period of market saturation. The linear decreasing
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Lecture 5 S11 - Lecture 5 Forecasting Source/1 D.R.Anderson...

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