03 Oct Highline Financial Service
CASE STUDY #1
Highline Financial Service offers three types of service to its client. Freddie Mack (Managing partner) has a data from three categories of services over the past eight quarters. Seem like other company’s factors have not changes “in terms of advertising or promotion, and competition doesn’t change” (Stevenson, William (2011-02-15). This data will be utilized to estimate demand for each service for the subsequent four quarters using Naive Forecast and Moving Average. Naive forecast “uses a single previous value of a time series as the basis of a forecast. But naïve forecast has one weakness of the naive method is that the forecast just traces the actual data, with a lag of one period; it does not smooth at all.” Stevenson, William (2011-02-15). Moving Average forecast “uses a number of the most recent actual data values in generating a forecast” Stevenson, William (2011-02-15).
As we can see each graph present for each term Highline Financial service provides. I will now analysis the whole graph using forecast. Service A seem to be increase in each quarter, service B seem to be decrease and service C is the most unstable one.
First I will use Naïve Forecast there are three items need to be figured MAD, MSE and MAPE. First step I will used period as a quarter and year like (1,2,3,4,5,6,7,8) then input the Service A numbers (60,45,100..). “With seasonal variations, the forecast for this “season” is equal to the value of the series last “season.””Stevenson, William (2011-02-15), so we have (60,45,..) for Naïve forecast. then calculated the Error by using actual (service A)- Naïve Forecast we have (-15,55,-25,..), next used |E| and sum the |E| we have 207. Then we will used Error2 and calculated it sum we have 8775. Then using |E| to calculated MAD=207divide for 7 is equal to 29.57, then calculate MSE=8775 divide for (7-1) is equal 1462.5. last used MAPE (Absolute Percentage Error)for each period |E| divide for ServiceA then sum all the result and divide for 7 (number of error)
| Naïve | ||||||||
| Period | Service A | Forecast | Error | |E| | Error2 | Absolute Percentage Error | ||
| 1 | 60 | |||||||
| 2 | 45 | 60 | -15 | 15 | 225 | 0.33 | ||
| 3 | 100 | 45 | 55 | 55 | 3025 | 0.55 | ||
| 4 | 75 | 100 | -25 | 25 | 625 | 0.33 | ||
| 5 | 72 | 75 | -3 | 3 | 9 | 0.125 | ||
| 6 | 51 | 72 | -21 | 21 | 441 | 0.4117 | ||
| 7 | 112 | 51 | 61 | 61 | 3721 | 0.5446 | ||
| 8 | 85 | 112 | -27 | 27 | 729 | 0.317 | ||
| 207 | 8775 | 2.6089 | ||||||
MSE=8775/(7-1)=1462.5
MAD=207/7=29.57
MAPE=2.6089/7=0.37.
Second I will use Moving Average to calculate MAD, MSE and MAPE to final compare and find out which method is the best used for this case, like above I will still used period as a quarter and year like (1,2,3,4,5,6,7,8) then input the Service A numbers (60,45,100,75..). For Moving average I used the average between 2 numbers. For example with a giving number (60+45) divide for 2 and equal 52.5 I have the second number for Moving Average continue we have (72.5, 87.5, 73.5, ….). Used all the same methods to Error, |E|, and Error^2 and Absolute Percentage Error.
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