WINTER 2007 MGTSC 352 LEC B1 > DOCUMENTS > LABS > LAB 2 - SIMPLE
FORECASTING MODELS
Lab 2 - Simple Forecasting Models
Course Documents
Lab2_SimpleForecastingModels (614.5 Kb)
Lab 2 - Forecasting
January 19, 2007
Agenda:
1. Forecasting using the simple models
2. Forecasting using Exponential Smoothing (ES) and Double Exponential
Smoothing (DES)
3. Performance measures
Forecasting using Simple Models
In order to complete this lab, you will need the file which contains the daily
closing prices of Nortel Networks stock from February 2, 2004, through
September 15, 2004.
The purpose of this lab is to review the material covered in class with an
emphasis on the forecasting tools that Excel offers.
Scenario
Way back in the year 2000, your Dad received a hot stock tip from his buddy
Larry. He trusted Larry so he bought a pile of Nortel stock for $115 a share.
Soon the stock started to plummet but your dad held on to his shares hoping
that he could recover some of his investment (Larry's body was found floating
in the North Saskatchewan a few months ago.) He rounded up what was left
of his life savings and has paid for you to go to University for the last few
years but now he needs some more money to help you in the new semester.
He heard you were in a fancy forecasting class and he would like you to help
him predict the stock price. Since your future at University depends on this
money, you decide you better pay attention to your forecast-loving
instructors, Morgan and Jen.....
In order to complete this lab you will need to use the data on the Nortel
worksheet, which contains the daily closing prices of Nortel Networks stock
from February 2, 2004, through September 15, 2004.
Given such data, how would you forecast the closing price of the stock on
September 16, 2004?
Forecasting
1) Last point: Ft+1 = Dt;
2) Historical average: Ft+1 = average(D1 , D2 , …, Dt)
3) Simple moving average (SMA): Ft+1 = average(Dt-m+1, Dt-m+2, ..., Dt),
where m="window", suppose m=4;
4) Weighted moving average (WMA): Ft+1 = w1  Dt-m+1 + w2  Dt-m+2 +
…+ wm  Dt, where the weights satisfy w1 + w2 + …+ wm = 1, suppose m = 4,
w1 = 0.1, w2 = 0.2, w3 = 0.3, w4 = 0.4;
5) Exponential smoothing (ES): F2 = D1, Ft+1 = LS  Dt + (1-LS)  Ft,
where 0  LS  1, suppose LS=0.25;
Initialization:
Learning:
6) Double Exponential smoothing (DES): Ft+1 = Lt + Tt. Lt = LS x Dt + (1LS) x (Lt-1 + Tt-1) and Tt = TS x (Lt - Lt-1) + (1 - TS) x Tt-1, where 0  LS and
TS  1. Suppose LS=0.25 and TS=0.25:
Initialization:
LEVEL... TREND......
Learning:
Prediction:
7) Which of all of the above forecasting methods would you recommend to
your Dad? You may want to compute some performance measures before
answering this question.
Error Calculations:




Error: the difference between the actual data and the forecasted data
Absolute error: the absolute value of the error
Percentage error: the absolute error divided by the actual data
Square error: the error to the power of 2
Performance measures
Note: n = number of data points one can calculate errors for
BIAS (average error): Compute the average of the errors.
MAD (mean absolute deviation): Calculate the average of the absolute errors.
MAPE (mean abs. percent error): Calculate the average of the % errors.
MSE (mean square error): Calculate the sum of the squared errors and divide
this by the total number of errors less one.
SE (standard error): Calculate the square root of the MSE.
Take a look at the other methods for calculating the performance measures. These can be found on
the completed tabs. Feel free to use any method you're comfortable with. That's the nice thing
about MGTSC... there are often many ways to come up with an answer!