Is there a way that we can reduce the variability in the output? One possibility to use a moving average of the raw values. Using an Exponentially Weighted Moving Average (EWMA) algorithm will tend to damp out small perturbations in the data, providing a more stable reading.
This moving average is called exponentially weighted because the weights given to previous values fall off exponentially. The immediately previous value is weighted more heavily than the value before that. All values figure into the current value, but as we go back in time, the weights for those old values become very, very small.
The core calculation for a weighted data point, 
, from the raw data point, 
, looks like this:
We used a weighting value, w, that expresses the influence of the previous data point on the current data point. If w is one, previous values have no influence. If w is zero, the initial value is the only one that matters and new values are ignored.
The very first data...
