I want to discuss a system I created which will do the following
 The Machine will use the history of the event and predict the next occurrence of the event. Weighted average pressure of history will be a major factor to the predictive logic.
 The Machine will record the actual occurrence of the event and measure the deviation of that occurrence with its prediction.
 Based on the deviation the Machine will adjust the logic for the next prediction of the event
 The Machine will continue to learn more about the event and wrap it in a logic.
This system reminds me of the principle of a success business, there is no such thing as failure. There are projects that “do not work out” but the successful business person looks at such events as a way to learn more about the consumer, to learn about what works and what does not. The Predictive system whom I will call Darmok is not afraid to make predictions about behavior, to make “mistakes”, it sees it as an opportunity to learn more about the person or system, to grow. Darmok will adjust its logic and get better.
Meet Darmok


Predicted  
Year 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 

Jan 
502 
503 
504 
505 
506 
507 
508 
509 
510 
511 
Calculations
X 
0 
1 
2 
3 
4 
5 
6 
7 
8 
9 
XY 
0 
501 
1004 
1509 
2016 
2525 
3036 
3549 
4064 
4581 
X2 
0 
1 
4 
9 
16 
25 
36 
49 
64 
81 
CyRltve 
99.60159 
99.6 
99.6 
99.6 
99.6 
99.6 
99.6 
99.6 
99.6 
99.6 
Xs  Ys  X2s  XYs  B1  B0  YT  YT2 
4.5 
504.5 
285 
22785 
1 
500 
510 
510.9961 
Jane Doe grocery bill for last 10 years Month of January. Based on the history, we want to predict grocery bill for Jan of the next year 2000 (YT). Also, if computer was predicting for the last 10 years, then we want to factor in the difference between the predicted amount and the actual to arrive at a more accurate prediction, YT2.
 The month in question for this sample is January
 The Predicted section shows data that was manually predicted (not by Darmok) for January for each year
X – For each year in the series, so increment for each new year (first year will be 0)
XY – X * the data value for the month (Say January)
X2 – X * X or X squared
CyRltve (a measure in Percentage of the deviation between actual and predicted value for the month) – (100*actual data value of the month)/(manual predicted data value of the month)
So for a new year lets say 2000 is the new year to predict
X = 10 (we have data from 10 previous years, so 2000 the 11year will be 10, we start count from 0. This is the source of the 10 you see in formulas below)
Xs – Sum of all the previous X/10
Ys – Sum of all the previous years data for the month/10
X2s – Sum of all the X2 for January for all the previous year
XYs – Sum of all the XY for January for all the previous year
B1 = (XYs10*Xs*Ys)/(X2s10*POWER(X2s,2))
B0 = Ys – (B1*Xs)
YT (Predicted value for Jan 2000) = B0 + B1 *10
YT2 (Predicted value for Jan 2000 factoring in the deviation of previous actual to previous predicted) = YT + ((Sum of all CyRltve for the month)/10)/100
The future of Darmok
I plan to use Darmok in MIM/FIM and many other systems to predict behavior. I will give an example of using Darmok to predict household expenses in the next blog post.