Time Series Analysis – ACCA Management Accounting (MA)
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Hi John, are we expected to to know the limitations of the model? If so, In the exam could I just say that it may be inaccurate because it uses historical data? are there any other limitations I should be aware of? Thanks John.
Firstly, you cannot be expected to write anything – all of the questions will be multiple choice etc..
If anything is asked them most likely is just the reason for the two different ways of calculating the seasonal variations (i.e. whether or not to calculate them as %’s).
(The fact that you are using historical data is a limitation of any forecast, but it not a limitation of the method because any method of forecasting will be based on historical data.)
In minute 14:02 of the time series analysis, how does 86, the average of 84.75 and 87.25 coincide with 82? If anything, it is closer to 87 than it is to 82. I do not understand in what respect the two figures coincide. Please elaborate
The average of 86 sits alongside the actual result of 82, and so they are comparable.
Hello.
For the final question, you brought down 91.35 instead of 95.35 which changes the end answer. Where you got 398.26 I got 400.26 which led to a decrease by 0.065 and then a sum of 400. Is that correct?
Dear John,
If the Question asks for the Average Seasonal Variances for the Quarters,
Should the answers be +0.06,+3.13,-3.94,+1.44 OR -0.1125,+2.9575,-4.1125,+1.2675?
Thanks!
The first (+0.06 etc) as explained in the lecture, if using the additive model.
sir, In 3rd quarters of 2000 while calculating seasonal variance averages, you have written 91.35%, instead of 95.35%. therefore if we use 95.35% for the 3rd quarters of 2000,the average quarters will be 100.01%,103.02%,95.68%,101.55% and the total is 400.26. therefore we need to subtract 0.26 to get 400 as total. we have to subtract 0.06 for the 1st and 2nd quarters and 0.07 for 3rd and 4th quarters. so the total of each quarters are, 99.95, 102.96,95.61,101.48 respectively
Hello Sir!
I understand how we get -0.17, (0.69/4= 0.17) but I do not understand -0.18 in 24:16 minute.
Could you explain it, please?
Thank you for considering my request.
It is just rounding. Because 0.69/4 = 0.1725, and I rounded it to 0.17, then they won’t add up to exactly 0.69. To make it happen I adjusted one of them by 0.18, so now they will all add up to 0.69.
Thk u so much mr john for your explanation.
I want to know why the total avaerge must add up to 400 and 0 using the multiplicative and additive model respectively?
With the multiplicative method, the variations are all bit more or a bit less than a 100. On average they are 100 and if there are 4 of them then the total must be 400.
With the additive model that are all a bit more than zero or a bit less than zero, so the average is zero and the total should be zero.
Thank you so much for your clear response
You are welcome 馃檪
you said in the video that to check that the centered averages are reasonably linear, we can put them on a graph and see if they are more or less liner but I want to ask could we not use correlation coefficient to check the linearity?
Yes – by all means.
Hi sir! In the video you said to use regression analysis to forecast trend. But could you please tell how? What will we take as variable x and what will we take as variable y?