Quote from: dree12 on September 28, 2014, 11:33:17 PM

Treat the logarithm of the time series as a function with domain [0, π).
Detrend the dataset.
Fit the function as a linear combination of sin(x), sin(2x), sin(3x), etc. to the desired precision.
Extrapolate this to the domain [π, 2π).
Retrend the dataset and remove the logarithm to get predicted price values.
Multiply by the "depression factor" to correct predicted price values so as to be consistent with today's price.

Since this algorithm has no skill, I feel comfortable sharing it as a curiosity. Does anyone else have any similar failed models that nevertheless produce interesting results?

You mean you changed the entire domain available at the time to [0, π)? Or do you change every year to [0, π), or something like that?

If it's the first, how do you refit the model as more data becomes available? Do you set the entire domain to [0, π) every time?

The "depression factor" bothers me. You mean, your model yields an expected price, which you have to change to current price?

Instead of doing that, try creating a model for the differences in price. That way, you won't need any depression factor. See the first model in my signature for an example.