1. A method comprising:
receiving, by a computer system comprising computer hardware, an identity of a metric of interest and a future time point;
retrieving, by the computer system, a prediction configuration previously associated with the metric of interest, the prediction configuration comprising a period combination;
wherein the period combination comprises a plurality of time periods, each time period comprises one or more segments, and each segment of the one or more segments comprises adapted historical values of the metric of interest incrementally inserted therein;
for each time period of the plurality of time periods:
the computer system identifying, for the future time point, a corresponding segment of the one or more segments;
accessing, by the computer system, a set of adapted historical values from the corresponding segment; and
computing, by the computer system, an intermediate predicted value from the set of adapted historical values, the computing comprising:
testing for a trend in the set of adapted historical values;
based on a result of the testing, determining whether the set of adapted historical values satisfies criteria for performing linear regression;
responsive to a determination that the set of adapted historical values satisfies the criteria for performing linear regression, computing the intermediate predicated value using linear regression; and
responsive to a determination that the set of adapted historical values does not satisfy the criteria for performing linear regression, computing the intermediate predicted value based on an average of the set of adapted historical values; and
calculating, by the computer system, a predicted value for the metric of interest based on the computed intermediate predicted value.
2. The method of claim 1, wherein the calculating comprises calculating a sum of each intermediate predicted value.
3. The method of claim 1, wherein:
the prediction configuration comprises a prediction algorithm previously associated with the metric of interest; and
the computing comprises utilizing the prediction algorithm.
4. The method claim 3, wherein the prediction algorithm is selected from the group consisting of: a least square linear regression algorithm, a Theil-Sen estimator, a prediction based on averages, and an exponential prediction algorithm.
5. The method of claim 1, comprising causing the predicted value to be output.
6. The method of claim 1, wherein, responsive to a determination that the set of adapted historical values meets specified criteria, the computing of the intermediate predicted value based on an average of the set of adapted historical values comprises calculating an average over a second half of the adapted historical values.
7. The method of claim 1, wherein the plurality of time periods are selected from the group consisting of: hour, day, week, and month.
8. An information handling system comprising:
at least one computer processor, wherein the at least one computer processor is operable to implement a method comprising:
receiving an identity of a metric of interest and a future time point;
retrieving a prediction configuration previously associated with the metric of interest, the prediction configuration comprising a period combination;
wherein the period combination comprises a plurality of time periods, each time period comprises one or more segments, and each segment of the one or more segments comprises adapted historical values of the metric of interest incrementally inserted therein;
for each time period of the plurality of time periods:
identifying, for the future time point, a corresponding segment of the one or more segments;
accessing a set of adapted historical values from the corresponding segment; and
computing an intermediate predicted value from the set of adapted historical values, the computing comprising:
testing for a trend in the set of adapted historical values;
based on a result of the testing, determining whether the set of adapted historical values satisfies criteria for performing linear regression;
responsive to a determination that the set of adapted historical values satisfies the criteria for performing linear regression, computing the intermediate predicated value using linear regression; and
responsive to a determination that the set of adapted historical values does not satisfy the criteria for performing linear regression, computing the intermediate predicted value based on an average of the set of adapted historical values; and
calculating a predicted value for the metric of interest based on the computed intermediate predicted value.
9. The information handling system of claim 8, wherein the calculating comprises calculating a sum of each intermediate predicted value.
10. The information handling system of claim 8, wherein:
the prediction configuration comprises a prediction algorithm previously associated with the metric of interest; and
the computing comprises utilizing the prediction algorithm.
11. The information handling system claim 10, wherein the prediction algorithm is selected from the group consisting of: a least square linear regression algorithm, a Theil-Sen estimator, a prediction based on averages, and an exponential prediction algorithm.
12. The information handling system of claim 8, comprising causing the predicted value to be output.
13. The information handling system of claim 8, wherein, responsive to a determination that the set of adapted historical values meets specified criteria, the computing of the intermediate predicted value based on an average of the set of adapted historical values comprises calculating an average over a second half of the adapted historical values.
14. The information handling system of claim 8, wherein the plurality of time periods are selected from the group consisting of: hour, day, week, and month.
15. A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising:
receiving an identity of a metric of interest and a future time point;
retrieving a prediction configuration previously associated with the metric of interest, the prediction configuration comprising a period combination;
wherein the period combination comprises a plurality of time periods, each time period comprises one or more segments, and each segment of the one or more segments comprises adapted historical values of the metric of interest incrementally inserted therein;
for each time period of the plurality of time periods:
identifying, for the future time point, a corresponding segment of the one or more segments;
accessing a set of adapted historical values from the corresponding segment; and
computing an intermediate predicted value from the set of adapted historical values, the computing comprising:
testing for a trend in the set of adapted historical values;
based on a result of the testing, determining whether the set of adapted historical values satisfies criteria for performing linear regression;
responsive to a determination that the set of adapted historical values satisfies the criteria for performing linear regression, computing the intermediate predicated value using linear regression; and
responsive to a determination that the set of adapted historical values does not satisfy the criteria for performing linear regression, computing the intermediate predicted value based on an average of the set of adapted historical values; and
calculating a predicted value for the metric of interest based on the computed intermediate predicted value.
16. The computer-program product of claim 15, wherein the calculating comprises calculating a sum of each intermediate predicted value.
17. The computer-program product of claim 15, wherein, responsive to a determination that the set of adapted historical values meets specified criteria, the computing of the intermediate predicted value based on an average of the set of adapted historical values comprises calculating an average over a second half of the adapted historical values.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for the treatment andor amelioration of urticaria comprising administration of a pharmaceutically active dose of a compound to a subject in need of such a treatment andor amelioration
wherein the compound has the following formula I
wherein
R1 is a C4-13 hydrocarbon group comprising a quarternary nitrogen atom;
X is O or a direct bond;
R2 is a C10-20 hydrocarbon group, wherein one or more hydrogens are optionally replaced by fluorine and wherein one or more CH2 groups are optionally replaced by oxygen, or a group of the following formula II
wherein Y is O, O(CO), S or S(CO);
R3 is OH, C1-4 alkyl, O\u2014C1-3 alkyl, O(CO)NH\u2014C1-3 alkyl, O(CO)\u2014C1-6 alkyl, S(CO)\u2014C1-6 alkyl, O(CO)\u2014C2-3 alkenyl or CH2O\u2014C1-3 alkyl;
R3\u2032 is H or C1-4 alkyl; and
R4 is a C10-20 hydrocarbon group, wherein one or more hydrogens are optionally replaced by fluorine.
2. The method of claim 1, wherein R1 is selected from one of the following formulae IIIa to IIIc:
wherein n1 is an integer from 1 to 7, and n2 is an integer of 1 or 2.
3. The method of claim 1, wherein X is O.
4. The method of claim 1, wherein R2 is a C10-20 hydrocarbon group, wherein one or more hydrogens are optionally replaced by fluorine, and wherein one or more CH2 groups are optionally replaced by oxygen.
5. The method of claim 4, wherein R2 is a C12-18 alkyl group.
6. The method of claim 1, wherein R2 is selected from formula II.
7. The method of claim 6, wherein Y is O.
8. The method of claim 6, wherein R3 is O\u2014C1-2 alkyl.
9. The method of claim 6, wherein R4 is a C12-18 alkyl group.
10. The method of claim 1, wherein the compound of formula I is selected from edelfosine, miltefosine, perifosine, ilmofosine, 1-O-palmityl-2-O-methyl-sn-glycero-3-phosphocholine and 1-O-palmityl-2-O-ethyl-sn-glycero-3-phosphocholine.
11. The method of claim 10, wherein the compound of formula I is miltefosine.
12. The method of claim 1, wherein said urticaria is selected from the group consisting of cholinergic urticaria, dermagraphism, cold urticaria, solar urticaria, aquagenic urticaria, drug-related urticaria and toxin-related urticaria.
13. The method of claim 1 wherein said subject is a human subject.