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Forecasting

Using some historical data for the past 40 periods (see attachment) I need to make a forecast for the next 12 periods (periods 41 thru 52) using whatever is the best/most accurate of the simple forecasting techniques we have covered so far in our 2nd year class.

These include: 1)Simple Moving Average 2)Weighted Moving Average 3)Exponential Smoothing 4)Linear Regression 5)Seasonal Forecasting via Regression, if we determine it is subject to seasonality (i.e., linear trends with multiplicative seasonality or nonlinear trends with multiplicative seasonality, and linear trends with additive seasonality or nonlinear trends with additive seasonality)

Using Excel or the Excel add-in Crystal Ball (we may NOT use Minitab, SAS, JMP, or any other packages), we need to investigate AT LEAST 3 different ways to forecast this data, and present the MAD (mean absolute deivaiton) and parameter values of each technique we tried. And state why we ended up going with a particular method as the most accurate.

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Hello:
<br>I used 3 methods to evaluate the data (as per your request). I started out by plotting the data values. Visual observation revealed cyclicity and a rising trend. So, I figured the best attack point would be a simple linear regression (leading up to ...

Solution Summary

Using some historical data for the past 40 periods (see attachment) I need to make a forecast for the next 12 periods (periods 41 thru 52) using whatever is the best/most accurate of the simple forecasting techniques we have covered so far in our 2nd year class.

These include: 1)Simple Moving Average 2)Weighted Moving Average 3)Exponential Smoothing 4)Linear Regression 5)Seasonal Forecasting via Regression, if we determine it is subject to seasonality (i.e., linear trends with multiplicative seasonality or nonlinear trends with multiplicative seasonality, and linear trends with additive seasonality or nonlinear trends with additive seasonality)

Using Excel or the Excel add-in Crystal Ball (we may NOT use Minitab, SAS, JMP, or any other packages), we need to investigate AT LEAST 3 different ways to forecast this data, and present the MAD (mean absolute deivaiton) and parameter values of each technique we tried. And state why we ended up going with a particular method as the most accurate.

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