Fixing Data Issues With Volumes in Timeseries

Dealing With Erroneous Volume Metrics

Exploring Imputation Solutions

1) Take simple average between periods. If there is only one volume data point missing between 2 others, you can take a simple "average" between the valid points to smooth out the missing. This method is far the simplest.
2) Carry-forward the previous time intervals' volume data point. This is also a simple, straight forward solution that carries forward the previous valid volume metric to fill in the missing point.
3) Discard the missing timeseries. Certain models may not need continuity between time intervals in order to fit the model. If this is the case, it may make sense to discard the missing values rather than training the model with a "synthetic" volume metric.
4) Use quantiles from the distribution to fit the missing data point(s). This technique is by far the most involved compared to the previous three. The goal is to find the 50th percentile from the volume distribution in order to find the most "central" or average value to use. This requires you to use a parametric function on the entire distribution of returns, and find the percentile that corresponds to the middle of the distribution (50%). Then you would use this value to impute the missing volumes.








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