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November 21, 2015 at 2:07 pm
Thank you John I understand very well now.
John Moffat says
November 21, 2015 at 2:08 pm
You are welcome Michelle 🙂
September 23, 2015 at 10:40 am
If by the additive model the seasonal variation will add up to 0.How about the multiplicative model……………will it add up to 0 too….or is there any criteria that is standardized upon which an expectation should be based.
September 23, 2015 at 10:58 am
The multiplicative seasonal variations will add up to 400. (Think about it – some are less than 100 and some are more than 100, and there are 4 of them 🙂 )
Have you actually watched the whole lecture, because I do actually say this in the lecture!!!
September 23, 2015 at 10:34 am
Hey Mr. Mr.Moffat i have a problem on this additive model.How will you do the forecasting on the basis of the additive model if for the multiplicative one,one has to multiply the average of the seasonal valuation of the respective quarter by the trend……….am i going to multiply the average valuation of the respective quarter by the trend likewise i think am lost in it all.
September 23, 2015 at 11:10 am
Yes – you multiply by the seasonal variation. I actually show this at the end of the lecture!!
August 24, 2015 at 1:42 pm
Can you kindly show me calculation for Q1 under TEST on pg 107 i agree with what you’ve said on the video that the amount for Q4 should be -7.6 but for exam purpose in case you are not given a question in multiple choice Question format
August 24, 2015 at 3:25 pm
Questions on time series will be in multiple choice format!
The seasonal variations should add up to zero (using the additive model).
So 5.8 – 8.4 + 10.2 + X = 0, where X is the season 4 variation.
It is then basic arithmetic to calculate X 🙂
June 27, 2015 at 1:58 am
uhh…our professor is a funny and decent man!
lol, sometimes I just laugh to tears by your humor:)
still got 20 lectures to watch,but I did enjoy it.
June 27, 2015 at 8:17 am
Thank you 🙂
November 16, 2014 at 12:52 pm
Sorry to bother you again.
The answer to the question from a June 2011 exam paper was 517 units (400+(10*7)*1.1, but the response received calculated to 480 units. Could you please explain how 1.1 was calculated.
November 16, 2014 at 1:05 pm
The ACCA does not publish past exams for Paper F2, so I do not know what the question was.
November 16, 2014 at 12:32 pm
Please ignore my question below. I just saw your response now.
November 15, 2014 at 3:02 pm
Would you please assist me with working out this question:
A company uses a multiplicative time series to forecast sales. The trend in sales is linear and is described by the following equation
Trend = 400 + 10 T
where T = 1 denotes the first quarter of 2010, T = 2 denotes the second quarter of 2010 etc.
The average seasonal variations are as follows:
Quarter 1 2 3 4
% Variation -30 +40 +10 -20
What is the sales forecast for the third quarter of 2011?
November 15, 2014 at 3:25 pm
The last quarter of 2010 is T=4. the first quarter or 2011 is T=5.
So…the third quarter is T=7
Put T = 7 in the equation and that will give you the trend.
Because the variation for quarter 3 is +10, add 10 to the trend and you will have the forecast.
November 21, 2015 at 11:49 am
Two years later I’m trying to figure out this exact question. Searched all over trying to get an explanation to the answer. This was my last stop before giving up. According to your working the answer is 480 and not 517?
The answer I saw to the question as well “(400+(10*7)*1.1”.
Grateful for you response.
November 21, 2015 at 1:40 pm
Sorry, I was wrong to say add 10 to the trend, because the seasonal variations are %’s (it is the multiplactive model).
So the forecast is that the trend is (400 + (10×7), then you add 10% to the total (which is the same as multiplying by 1.1) for the seasonal variation.
October 28, 2014 at 9:21 pm
I perfectly understand the calculation of time series and seasonal variations. But can you please explain me how this variation is help full for me. Like if i expecting average sales for next year 250,000 every quarter i might forecast the quarterly sales by the variation. Like Q1 95 % of 250,000 etc….
October 29, 2014 at 8:30 am
It can be useful in budgeting if you are wanting to forecast sales per quarter.
It is also used by governments etc when looking at such things as number of people unemployed. In the UK, for example, unemployment is always lower in summer because people get temporary work in farms. It is therefore not valid to compare the numbers for spring and summer and say that overall unemployment is falling. So they adjust the figures for seasonal variations and it is then a better comparison.
October 30, 2014 at 6:47 am
Thanks alot. I really enjoy your lecture. Wish to attend one of them.
October 12, 2014 at 9:43 am
Hi, in Moving Averages – Example 1. (pg 100).
I went ahead and try to verify the trend vs actual for 2002 Q3.
We figured that the average seasonal variation for Q3 is -3.94 and I calculated the average trend increment is 3.04.
Trend in 2002 Q2 was 107.25, therefore trend in 2002 Q3 should be 107.25 + 3.04 = 110.29.
Actual in 2002 Q3 should be 110.29 (Trend) + -3.94 (Avg seasonal variation) = 106.35. However, the actual sales that we were given was 103.
Could you please let me know what is causing the discrepancy? or if I’m doing this incorrectly.
Thanks a lot.
October 12, 2014 at 10:29 am
Both the average trend increment and the average seasonal variations are precisely that – averages.
It would be a miracle if the actual trend increment were exactly the same each quarter, or if the seasonal variations were exactly constant. If they were then we would not have needed to do all the work calculating the averages 🙂
October 13, 2014 at 1:07 pm
Thank you very much for the help.
October 13, 2014 at 4:43 pm
You are welcome 🙂
August 25, 2014 at 12:34 pm
I found it really useful to put the numbers in excel and do the calculations (using =AVERAGE) to show the data graphically. Plot the MAs and CAs on a chart too to graphically show the variances.
October 12, 2014 at 10:30 am
Great – thats a good exercise (but obviously not in the exam 🙂 )
August 23, 2014 at 10:09 am
Brilliant lecture by an equally dashing Professor, if I do say so myself. Thanks again Sir John Moffat!
Thank you, Bhagat 🙂
abhinandh dileep says
November 29, 2013 at 4:57 pm
thanks alot for the video lectures… its very helpful…
October 31, 2013 at 5:52 pm
Hello John i completely understand the calculations of this topic but i never get one thing why in the end we have to Sum it to Zero ? please help
October 31, 2013 at 6:53 pm
It is because the seasonal variations are all above or below the averages. Some are higher than everage and some are lower that average, and because of the they should add up to zero.
Does that make sense? 🙂
November 1, 2013 at 1:37 pm
Yes thank you so much…. one little thing we have alot of questions in our revision kit on multiplicative model and i cant find a video explaining that, i just want to know is it important and can we expect a lot of questions based on that in exams …… thanks you =)
November 1, 2013 at 1:48 pm
The multiplicative model is covered in this video (after the additive model).
I would not expect there to be lots of questions on time series, but there are certainly likely to be some questions – one either or both of the two models.
November 2, 2013 at 1:25 am
ok thank you so much =)
Rana Nabeel says
June 30, 2014 at 4:42 pm
Slam… These should add up to zero as the trend is between high and low averages, and we have four quarters in a year among them two are higher than average and two are lower than average … so if we see the scenario in an “ideal way” then we come to conclusion that with the value the trend gets higher in “first quarter” it should get lower with same value in the 2nd quarter .. and respectively same in 3rd and 4th quarter so adding up these should result in a perfect zero(Ideal Case)..
Hope the answer of the question is satisfactory.. 🙂
September 12, 2013 at 8:18 pm
Hello John, just for myself to not be confuse is there mistake done in sample 2 calculation multiplicative method? quarter 4: actual / trend should be 98% and in your case its 102% I presume you divide trend/ actual? I am wrong?
September 13, 2013 at 7:32 am
In quarter 4 of 2000, the actual is 90 and the trend is 88.25.
You divide actual by the trend, so 90/88.25 = 102.0%
August 25, 2013 at 1:19 pm
Could you please show me how to forecast the trend of the frist quater in 2003 using additive model and multiplicative model. I do appreciate it ?^_^
August 25, 2013 at 7:25 pm
I think you mean slightly different from what you wrote 🙂
What you do to make a forecast is first of all forecast the trend (and this would be the same whether you were using multiplicative or additive), and then you would adjust the trend forecast by the seasonal variation to get the ‘actual’ forecast.
There are several ways you could forecast the trend, but for exams you would effectively use the high low method. In example 1, the trend values increase from 86.00 to 107.25 (which is an increase of 21.25) over 7 quarters (although their are 8 values, there are 7 increases). So on average it is increasing by 21.25 / 7 = 3.036 per quarter.
If we want a forecast for qtr 1 of 2003, then this is 3 quarters away from the last trend value we have (quarter 2 of 2002) and so we take 107.25 + (3 x 3.036) = 116.36.
This is a trend forecast, but we need now to adjust by the seasonal variation.
Using the additive model, in qtr 1 the actual is on average 0.06 more than the trend, so the forecast would be 116.36 + 0.06 = 116.96
Using the multiplicative model, qtr 1 is 100% of the trend, and so the forecast would be 100% x 116.36 = 116.36
August 26, 2013 at 2:07 am
Thank you so much! I am clear now.
December 27, 2016 at 2:40 pm
thank u so much sir… this was the only problem i had with this chapter that u explained and I quote, “although their are 8 values, there are 7 increases”. nobody could explain that to me before. thank u so much and great lecture sir. 🙂
December 27, 2016 at 8:42 pm
Thank you for the comment 🙂
Musa Bin Masood says
February 9, 2013 at 8:51 pm
ttotal should be total zero can’t understand the logic ??
February 10, 2013 at 10:03 am
Some are higher than average and some are lower than average – however the total difference should be zero.
Suppose you had two numbers – 70 and 80. The average of the two is 75.
The difference between the first number and the average is -5
The difference between the second number and the average is +5
The total of the differences = -5 +5 = 0
December 4, 2012 at 5:03 pm
To calculate the moving average is not clear .How to calculate to get 86
January 22, 2013 at 5:29 pm
November 16, 2012 at 6:10 am
what is meant by perfect world sir???
November 20, 2012 at 6:51 am
@ryanpieblock, Perfect world implies that there are ideal conditions, ie, everything goes just as we expect it to.
August 15, 2012 at 12:59 pm
perfect lecture ever !!!!
July 30, 2012 at 8:38 pm
Thanku Open Tuition was breaking my head trying to understand Seasonal Variations,after watching this I understood in 5 mins..U r grt help for people doing self study..Thanku again.
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