The following table shows predicted product demand using your particular forecasting method along with the actual demand that occurred:

$\backslash$table$[[$FORECAST$,$ACTUAL$],[1,480,1,530],[1,380,1,480],[1,680,1,580],[1,730,1,630],[1,780,1,680]]$

Compute the tracking signal using the mean absolute deviation and running sum of forecast errors.

Note: Negative values should be indicated by a minus sign. Round your "Mean Absolute Deviation", "Tracking Signal" to $2$ decimal places and all other answers to the nearest whole number.

$\backslash$table$[[$Period$,$Forecast,Actual,Deviation,RSFE,$\backslash$table$[[$Absolute$],[$Deviation$]],\backslash$table$[[$Sum of$],[$Absolute$],[$Deviation$]],$MAD,TS$],[1,1,480,1,530,-50,-50,50,50,50\mathrm{.}00,-1\mathrm{.}00],[2,1,380,1,480,-100,-150,100,150,75\mathrm{.}00,-2\mathrm{.}00],[3,1,680,1,580,100,-50,100,250,83\mathrm{.}33,-0\mathrm{.}60],[4,1,730,1,630,100,50,100,350,87\mathrm{.}50,0\mathrm{.}57],[5,1,780,1,680,100,150,100,450,90\mathrm{.}00,1\mathrm{.}67]]$Deviation$,$ RSFE and TS are wrong