I have long resisted the inclusion of data from exchanges other than Gox because I never really understood how to include samples of different lengths into a model. But now that the volumes of Gox, Bistamp, and Btcchina have been comparable for so long I am forced to include their trade data into a model.
It may be sufficient to simply truncate the trade data to the shortest sample, but I really hate to throw away data. As well, I expect there will occasionally be cases where there will be missing data ongoing.
I wonder, how have some of you dealt with multiple data streams, and how to match them up, either through truncation, imputation, or some other means.
It may be sufficient to simply truncate the trade data to the shortest sample, but I really hate to throw away data. As well, I expect there will occasionally be cases where there will be missing data ongoing.
I wonder, how have some of you dealt with multiple data streams, and how to match them up, either through truncation, imputation, or some other means.
You'll want to use the exciting science of reverse imputation. This complex mathematical technique uses the desired solution to inform the chosen imputation algorithm and data-source weighting coefficients.
Come on, get with its GUARANTEED to make Bitcoin look awesome! We know this from seeing the CPI numbers.