Define a proper strategy to deal with missing/incorrect values

[paulroujansky]

• In case of missing values, one solution would be to (1) pick the last available value up to 24h before missing value (for instance) - taking care not to use the last copied value as a good initial value. We should go backward in time for this reason. (using pandas.DataFrame.fillna() method for instance) - this part was implemented in commit 36dbc58.
  After doing so, we would then (2) delete any entry that would contain any NaN value.
  Yet we could imagine many other methods to deal with missing values (interpolation when it is potentially realistic or average-value filling etc).

• In case of incorrect/aberrant values, we should define a proper strategy to (1) spot them and (2) deal with them. Feel free to propose any method !