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That's true, of course. If the technique worked here then it would be because there really are location independent features that can learned. I view it as simi
by markbnj 7y ago
That's true, of course. If the technique worked here then it would be because there really are location independent features that can learned. I view it as similar to translating and transforming images in the MNIST digit set to account for the various ways the feature you're searching for can be spatially oriented. Of course I have no idea if this holds true and will work for pricing data.
- ska 7y agoRight, the thing about applications like the digit set and similar OCR problems etc., is that we can independently generate a model of "acceptable" translations/rotations and validate it reasonably easily because we understand the domain well (not that you can't cause trouble this way). This certainly isn't true across data sets.
- short_sells_poo 7y agoGenerating data this way is extremely difficult. The true population characteristics of prices are unobservable, we can only with with samples of the population. This means that any attempt at generating data is highly likely to add errors that are difficult to quantify. This is a fundamental difference to disciplines where ML is most successful: in finance you can’t meaningfully generate new data, you can only work with what has been historically recorded, and that is often not relevant to how the market will behave in the future. You can always generate more cat images for a ConvNet to learn, you can feed it photos from multiple angles or even 3D imagery. None of this is available for market data unfortunately.