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Deriving importance factors on a per-week basis #16
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Thanks, yes this is a good idea. It should increase accuracy. Pretty easy to implement: in the pre-processing stage we can generate all deltas up to N days and include all the input features spanning N-days. However, I think there should be some decay applied to the weight of older features, because the further you go in time, once effect peaks, the less relevant inputs should become (just a hunch). Also: I think the accuracy can benefit from some random shuffling of sample-orders and stacking them. In online learning, early examples have an advantage because learning rate decays with time. Currently I sort by abs(delta) which makes the big delta examples more important. I'll try to tackle this when I get some more free time. |
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Added support for any number of days history. This increases the number of data-points to train on, and hopefully reduces variance and random daily-noise. Currently the default history is set to 3 days ( I also restored the
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Daily weight changes are considered a very unreliable metric, prone to unpredictable fluctuations. Additionally, some factors may not be reflected immediately within 24h, but have a more long-term effect.
It would be great to take the same data and aggregate both factors and weight delta on a per-week basis, and then see whether the resulting factors are different from the existing daily results. Using the data you already have, it may give some new insights.
cc @arielf
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