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Ch 3:. Forecasting: Techniques and Routes. Study objectives. After studying this chapter the reader should be able to: Evaluate the suitability of several quantitative forecasting techniques for a given project
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Ch 3: Forecasting:Techniques and Routes
Study objectives • After studying this chapter the reader should be able to: • Evaluate the suitability of several quantitative forecasting techniques for a given project • Employ a selected technique or combination of techniques to forecast cash flows for a given project • Identify a suitable forecasting route for estimating cash flows for a given project.
Forecasting with time-trend projections • One basic requirement when using regression analysis for forecasting is the availability of predictions for the explanatory variable or variables. • When such predictions are not available and when the time series exhibits a long-term trend, time-trend projections can be used for forecasting • The time-trend method may be viewed as a special case of simple regression analysis where the independent variable is ‘time’.
Forecasting using smoothing models • The earlier discussion of regression and time-trend models applies to situations where the historical time series exhibits a trend. • When the historical time series does not exhibit a significant trend, smoothing models can be used for forecasting because these models adapt well to changes in the level of the time series. • These models are particularly suitable for situations where the more recent observations are more indicative of future values
Forecasting using smoothing models Advantages • Easy to use • Provide reasonable forecasts for the short- to medium-term forecasting periods Disadvantages • They will not catch turning points since the basis of the forecast is nothing but a weighted average of the historical data. • They are not suitable for a project concerned with a new product
smoothing modelsSimple moving average (SMA) • Consider the hypothetical sales data for the past twelve years in Table 3.6, There is no trend apparent in these data. • The simple moving average (SMA) uses the average of the nmost recent values in the time series as the forecast for the next period. Choosing n = 3, three-year moving averages have been calculated using Excel and are shown in Table 3.6
Table 3.6. Hypothetical sales data and calculation of simple moving average
smoothing modelsSimple moving average (SMA) How to choose the value of n? • If four-year moving averages are used, different forecasts will be obtained. • In practice, values of nin the range three to five are often used. • The Excel spreadsheet makes it easy to obtain sets of moving averages using different values for n. Then, the n which yields the minimum MSE can be selected for calculating the moving averages
smoothing modelsWeighted moving average (WMA) • In SMA, each observation in the calculation receives equal weight. • In the weighted moving average (WMA), different weights are assigned to the values in the time series. • For example, if the decision-maker believes that recent values are more important than less recent ones in arriving at forecasts, greater weight can be given to these.
smoothing modelsWeighted moving average (WMA) • For example, using the sales data set in Table 3.6, the sales for year 13 could be forecast by taking the three-year WMA. The allocation of weights is as follows: • A weight of 0.6 for the most recent observation (which is 49,000), 0.3 for the next older observation (which is 41,000) and 0.1 for the oldest observation (which is 50,000). Then the three-year WMA for the period of years 10–12 is:
Exponential smoothing • The basic exponential smoothing model is: 0.2 x 49000 + (1 - 0.2) x 42744 =
Exponential smoothing • The basic exponential smoothing model is: • Note that the last actual sales is 49,000 for year 12, so we use it for the three years.
