Filling the gaps with random forest

One of the main problems with different datasets is the missing data. Data that only have some annotation that points towards its existence but is missing. For example in the case of time series data, missing data will be missing values in the middle of the series. Values most likely could be inferred by just looking at the graph, yet an approximation of those values will generate a new and more concise data set.

These gaps slide through time and after some time the entire globe can be sampled. In this case, there are two sources of missing data, missing days where the data does not exist and missing locations. To overcome the missing days first and day index is created and a file name is attached to that index. But if the file is missing the previously sampled file will be attached to that day. This approach will be able to handle small gaps, but if the gaps are of several days then the data will look as if it freezes for a brief period.

This index will facilitate the processing to fill the location gaps. One simple form to approximate the missing data…

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