APPARATUS AND METHOD FOR DETERMINING A PROJECTED OCCURRENCE

1 . An apparatus for determining a projected occurrence, the apparatus comprising:

at least a processor; and

a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least a processor to:

identify a series of nonadjacent occurrences within process data;

determine a plurality of characteristic features corresponding to occurrences in the series of non-adjacent occurrences using a machine learning process, wherein the machine learning process further comprises:

training a machine learning model on a training dataset including a first plurality of non-adjacent occurrences as inputs correlated to a first plurality of characteristic features corresponding to occurrences; and

outputting the plurality of characteristic features corresponding to occurrences in the series of non-adjacent occurrences using the trained machine learning process;

generate a plurality of potential projected occurrences as a function of the plurality of characteristic features;

weight each potential projected occurrence of the plurality of potential projected occurrences as a function of at least an optimization constraint in the process data; and

select at least one projected occurrence as a function of the weighted plurality of potential projected occurrences.

2 . The apparatus of claim 1 , wherein the memory further contains instructions configuring the at least a processor to:

generate an unsupervised machine learning model as a function of a feature learning algorithm; and

determine the plurality of characteristic features using the unsupervised machine learning model.

3 . The apparatus of claim 1 , wherein the memory further contains instructions configuring the at least a processor to determine the plurality of characteristic features using K-means clustering.

4 . The apparatus of claim 1 , wherein:

the memory contains instructions configuring the at least a processor to identify the series of nonadjacent occurrences as a function of a first occurrence of the series of nonadjacent occurrences and a second occurrence of the series of nonadjacent occurrences; and

the first occurrence and the second occurrence include communications utilizing different communication channels.

5 . The apparatus of claim 1 , wherein generating the plurality of potential projected occurrences comprises:

training a potential projected occurrence machine learning model on a first training dataset including a first plurality of example characteristic features as inputs correlated to a first plurality of example potential projected occurrences as outputs; and

generating the plurality of potential projected occurrences as a function of the plurality of characteristic features using the trained potential projected occurrence machine learning model.

6 . The apparatus of claim 5 , wherein the memory contains instructions configuring the at least a processor to:

obtain a second training dataset as a function of the projected occurrence, wherein the second training dataset includes a second plurality of example characteristic features as inputs correlated to a second plurality of example potential projected occurrences as outputs; and

retrain the potential projected occurrence machine learning model using the second training dataset.

7 . The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to gather a datum as a function of the projected occurrence prior to the projected occurrence.

8 . The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to schedule a calculation as a function of the projected occurrence prior to the projected occurrence.

9 . The apparatus of claim 1 , wherein generating the plurality of potential projected occurrences comprises determining a probability of each potential projected occurrence of the plurality of potential projected occurrences.

10 . The apparatus of claim 1 , wherein selecting the at least one potential projected occurrence comprises determining which potential projected occurrence of the plurality of potential projected occurrences has the highest weighting.

11 . A method of determining a projected occurrence, the method comprising:

using at least a processor, identifying a series of nonadjacent occurrences within process data;

using at least a processor, determining a plurality of characteristic features corresponding to occurrences in the series of non-adjacent occurrences using a machine learning process, wherein the machine learning process further comprises:

training a machine learning model on a training dataset including a first plurality of non-adjacent occurrences as inputs correlated to a first plurality of characteristic features corresponding to occurrences; and

outputting the plurality of characteristic features corresponding to occurrences in the series of non-adjacent occurrences using the trained machine learning process;

using at least a processor, generating a plurality of potential projected occurrences as a function of the plurality of characteristic features;

using at least a processor, weighting each potential projected occurrence of the plurality of potential projected occurrences as a function of at least an optimization constraint in the process data; and

using at least a processor, selecting at least one projected occurrence as a function of the weighted plurality of potential projected occurrences.

12 . The method of claim 11 , wherein the method further includes:

using at least a processor, generate an unsupervised machine learning model as a function of a feature learning algorithm; and

using at least a processor, determine the plurality of characteristic features using the unsupervised machine learning model.

13 . The method of claim 11 , wherein the plurality of characteristic features is determined using K-means clustering.

14 . The method of claim 11 , wherein:

the method further includes identifying the series of nonadjacent occurrences as a function of a first occurrence of the series of nonadjacent occurrences and a second occurrence of the series of nonadjacent occurrences; and

the first occurrence and the second occurrence include communications utilize different communication channels.

15 . The method of claim 11 , wherein generating the plurality of potential projected occurrences comprises:

training a potential projected occurrence machine learning model on a first training dataset including a first plurality of example characteristic features as inputs correlated to a first plurality of example potential projected occurrences as outputs; and

generating the plurality of potential projected occurrences as a function of the plurality of characteristic features using the trained potential projected occurrence machine learning model.

16 . The method of claim 15 , wherein the method further comprises:

obtaining a second training dataset as a function of the projected occurrence, wherein the second training dataset includes a second plurality of example characteristic features as inputs correlated to a second plurality of example potential projected occurrences as outputs; and

retraining the potential projected occurrence machine learning model using the second training dataset.

17 . The method of claim 11 , further comprising gathering a datum as a function of the projected occurrence prior to the projected occurrence.

18 . The method of claim 11 , further comprising scheduling a calculation as a function of the projected occurrence prior to the projected occurrence.

19 . The method of claim 11 , wherein generating the plurality of potential projected occurrences comprises determining a probability of each potential projected occurrence of the plurality of potential projected occurrences.

20 . The method of claim 11 , wherein selecting the at least one potential projected occurrence comprises determining which potential projected occurrence of the plurality of potential projected occurrences has the highest weighting.