Currently working on an inventory forecasting implementation that takes sales history then forecasts it into the future based on a best fit from a range of different statistical algorithms.
Having read Nassim Talebs "The Black Swan" and "Fooled by Randomness" http://www.fooledbyrandomness.com/ i was initially skeptical that that approach would work or be even vaguely accurate. Surely in these uncertain times trend is dead and forecasting is a waste of time? Isn't 'gut feel' more important than some numbers in a spreadsheet?
To my surprise when back tested it forecasted within 10% of actual sales.
This lead me to a significant conclusion and raised two questions.
Having read Nassim Talebs "The Black Swan" and "Fooled by Randomness" http://www.fooledbyrandomness.com/ i was initially skeptical that that approach would work or be even vaguely accurate. Surely in these uncertain times trend is dead and forecasting is a waste of time? Isn't 'gut feel' more important than some numbers in a spreadsheet?
To my surprise when back tested it forecasted within 10% of actual sales.
This lead me to a significant conclusion and raised two questions.
1. Conclusion - My maths isn't up to scratch, i have subsequently re-enrolled to university mathematics course to fix this.
2. Question - How can I benchmark forecasted results based on past performance to check if the results may be worth using?
3. Question - Can i relate this concept to something more interesting than ERP?
At that point I decided to apply the concept to AFL football tipping.
The NAB bank has a footballing tipping comp http://tipping.afl.com.au/
I signed up.
To calculate past performance i would use the teams position on the ladder at the end of the home and away season. i.e. for each round I would pick the winner of each game based on who finished higher on the ladder in 2008.
Based on this simple method as at Round 7 i am currently ranked 5,810 out of 168,180 tippers in the 2009 AFL home and away season!
Now for the benchmarking.
1. How does this stack against the other punters in the comp?
2. How does this stack against pure chance (i.e. flipping a coin to pick the winner)?
8 Games per Round (max 8 correct picks)
Average Wins per round predicated by simple method outlined above 5.4 wins per round
Average Wins picked by all contestants for entire competition 4.4 wins per round
The simple method significantly outperforms the average footy tipper with all their experience, judgment and freely available information available at their fingertips.
Faith in forecasting restored (and lack of faith in the 'wisdom of the crowds' http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds reinforced).
2. Question - How can I benchmark forecasted results based on past performance to check if the results may be worth using?
3. Question - Can i relate this concept to something more interesting than ERP?
At that point I decided to apply the concept to AFL football tipping.
The NAB bank has a footballing tipping comp http://tipping.afl.com.au/
I signed up.
To calculate past performance i would use the teams position on the ladder at the end of the home and away season. i.e. for each round I would pick the winner of each game based on who finished higher on the ladder in 2008.
Based on this simple method as at Round 7 i am currently ranked 5,810 out of 168,180 tippers in the 2009 AFL home and away season!
Now for the benchmarking.
1. How does this stack against the other punters in the comp?
2. How does this stack against pure chance (i.e. flipping a coin to pick the winner)?
8 Games per Round (max 8 correct picks)
Average Wins per round predicated by simple method outlined above 5.4 wins per round
Average Wins picked by all contestants for entire competition 4.4 wins per round
The simple method significantly outperforms the average footy tipper with all their experience, judgment and freely available information available at their fingertips.
Faith in forecasting restored (and lack of faith in the 'wisdom of the crowds' http://en.wikipedia.org/wiki/The_Wisdom_of_Crowds reinforced).
