Neural Network Prediction: a Narrative Explanation

Discussion in 'Automated Trading' started by maxdama, Jul 26, 2008.

I appreciate your feedback

Poll closed Aug 2, 2008.
  1. Thanks- That made NNs easier to understand

    0 vote(s)
  2. Hmm- I didn't really get it

    0 vote(s)
  3. Um- The creative approach didn't ring my bell

    0 vote(s)
  4. Hey- Read my comment below

    1 vote(s)
  1. maxdama


    I wrote the following story to help beginners understand neural networks. I've noticed on ET that many people have a vague idea that NNs are very powerful but not a very good understanding of how they work. This story is about how the backpropogation training technique can be used to create a NN that makes accurate predictions. I will probably eventually write a second part to the story to illustrate a different cutting edge NN training system which has shown even better results than BP.

    This story was originally posted a few days ago on

  2. My .02 cents.

    I like your writing style and the metaphorical analogies you made.

    However, I think it would be extremely difficult for someone (who hasn't built a NN previously) to translate the narrative into a quantitative running model of a NN.

    Reminds me a bit of the journals from the myst. You could have hidden diagrams and tables of the advisor's inputs and elaborate drawings of mechanism's behind how his methodology operated.

    It would be nice if you also made an analogy of how he went about picking the original set of liars, scouts, and thieves from a universe of many, many potential liar, scouts and thieves (as many as there are grains of sand in the universe). Therein lied the true key to the wise Nyeralneht's success.

    Good Job. I'd give you a hand clap emoticon if they had one.
  3. maxdama



    Thanks, I certainly agree that it would be impossible to create a quantitative NN from scratch based on the story.

    Input selection heuristics would be a good addition. While writing I also realized it would have been more complete to explain how the hidden layers [advisors] are necessary to give it non-linear prediction ability, and also to mention threshold functions- sigmoidal etc. There are quite a few fundamentally necessary concepts to cover with NNs which makes them challenging to explain without getting bogged down in the details.

  4. "While writing I also realized it would have been more complete to explain how the hidden layers [advisors] are necessary to give it non-linear prediction ability"

    hmm... I would argue that one simple advisor ( a differential comparator with the error used to control feedback weight) would be sufficient to describe a non-linear prediction ability (albeit limited).
    But I digress.

    I like your blog as well. I always find regime changes fascinating, but extremely subjective (i.e. at what point does the regime change).

    This is the problem with things like hurst analysis, the resulting value and corresponding market type (trading/trending) is only useful in retrospect. At least from my observations.