Use and construction of time series interactions in a predictive model
A gene is disclosed for use in a predictive genetic algorithm that performs time series interactions between dataset variables. The temporal logic that performs the interaction is encoded as a binary string.
1 . A temporal gene for use in a chromosome of a genetic algorithm comprising:
a series selection component for determining which series from a dataset will interact;
a temporal selection component for determining a start location value and an end location value that map to variables in said selected series; and
an operator selection component for determining which operator from a predefined list of operators will be used to interact with variables from said start location value to said end location value, wherein the temporal gene returns a result from said interaction between said operator and said variables from said start to said end locations.
2 . The temporal gene according to claim 1 wherein said series selection component, said operator selection component, and said temporal selection component experience evolution.
3 . The temporal gene according to claim 2 wherein said series selection component, said operator selection component, and said temporal selection component are encoded as binary numbers from integer numbers prior to evolution.
4 . The temporal gene according to claim 3 wherein said operator selection component value corresponds to an indexed library of functions.
5 . The temporal gene according to claim 4 wherein said operator selection component value corresponds to one of a slope, a predicted error, a minimum, a minimum index, a maximum, a maximum index, and an average function.
6 . The temporal gene according to claim 5 further comprising a coefficient gene for holding a predetermined value.
7 . The temporal gene according to claim 6 wherein said predetermined value is used as a multiplier for another gene.
8 . The temporal gene according to claim 6 wherein said predetermined value is an output of another gene.
9 . The temporal gene according to claim 6 wherein said predetermined value is a weighting factor.
10 . The temporal gene according to claim 6 wherein said coefficient gene value is multiplied with said result.
11 . The temporal gene according to claim 10 wherein said coefficient gene is encoded as a binary number from an integer number prior to evolution.
12 . The temporal gene according to claim 11 wherein said series selection component, said operator selection component, said temporal selection component and said coefficient gene are decoded to integer numbers from binary numbers after evolution.
13 . The temporal gene according to claim 12 wherein modular arithmetic is applied to said series selection component, said operator selection component and said temporal selection component after evolution to validate that the evolved values of said series selection component, said operator selection component and said temporal selection component are each within a respective predetermined range of variable values.
14 . A method of creating temporal interactions for use in a genetic algorithm as a temporal gene comprising:
providing a series selection component for determining which series from a dataset will interact;
providing a temporal selection component for determining a start location value and an end location value that map to variables in said selected series; and
providing an operator selection component for determining which operator from a predefined list of operators will be used to interact with variables from said start location value to said end location value, wherein the temporal gene returns a result from said interaction between said operator and said variables from said start to said end locations.
15 . The method according to claim 14 further comprising evolving said series selection component, said operator selection component and said temporal selection component.
16 . The method according to claim 15 further comprising encoding said series selection component, said operator selection component and said temporal selection component as binary numbers from integer numbers prior to evolving.
17 . The method according to claim 16 wherein encoding further comprises concatenating said series selection component binary number with said operator selection component binary number and with said temporal selection component binary number.
18 . The method according to claim 16 further comprising providing a coefficient gene for holding a predetermined value.
19 . The method according to claim 18 wherein said predetermined value is used as a multiplier for another gene.
20 . The method according to claim 18 wherein said predetermined value is an output of another gene.
21 . The method according to claim 18 wherein said predetermined value is a weighting factor.
22 . The method according to claim 18 further comprising multiplying said coefficient gene value with said result.
23 . The method according to claim 22 further comprising encoding said coefficient gene as a binary number from an integer number prior to evolution.
24 . The method according to claim 23 further comprising decoding said series selection component, said operator selection component, said temporal selection component and said coefficient gene to integer numbers from binary numbers after evolving.
25 . The method according to claim 24 further comprising:
creating an integer number for said series selection component from a number of bits corresponding to the number of bits used to form its binary number;
creating an integer number for said operator selection component from a number of bits corresponding to the number of bits used to form its binary number;
creating integer numbers for said temporal selection component start location and end location from a number of bits corresponding to the number of bits used to form its binary numbers; and
creating an integer number for said coefficient gene from a number of bits corresponding to the number of bits used to form its binary number.
26 . The method according to claim 25 further comprising applying modular arithmetic to said series selection component, said operator selection component and said temporal selection component after evolving for validating that the evolved values of said series selection component, said operator selection component and said temporal selection component are each within a respective predetermined range of variable values.
27 . The method according to claim 26 further comprising assembling a logic statement for the temporal gene from said start and end locations defined by said temporal selection component and said operator selection component, wherein said logic statement provides said result.
28 . A method of creating temporal interactions for use in a genetic algorithm as a time gene comprising:
providing a series selection component for determining which series from a dataset will interact;
providing a temporal selection component for determining a start location value and an end location value that map to variables in said selected series;
providing an operator selection component for determining which operator from a predefined list of operators will be used to interact with variables from said start location value to said end location value,
providing a coefficient gene for holding a predetermined value;
assembling a logic for returning a result from said selected operator and said temporal selection component start and end location values; and
multiplying said predetermined value with said result.