METHODS AND APPARATUS TO PREDICT IN-TAB DROP USING ARTIFICIAL INTELLIGENCE

1 . An apparatus comprising:

an interface to obtain (A) contextual data obtained from a server and (B) validated in-tab totals, the validated in-tab totals corresponding to a number of meters in a location that have transmitted metering data within a threshold duration of time;

a filter to filter at least one of the contextual data based on the location; and

a model trainer to train a model using filtered contextual data and the validated in-tab totals, the model trainer to train the model to estimate an in-tab total for the location based on input contextual data corresponding to the location.

2 . The apparatus of claim 1 , wherein the contextual data includes that that corresponds to information that may result in a meter dropping out-of-tab.

3 . The apparatus of claim 1 , wherein the threshold duration of time is a first threshold duration of time, the contextual data corresponding to a second threshold duration of time from when the validated in-tab totals were obtained.

4 . The apparatus of claim 1 , further including:

a model implementor to implement the model to estimate the in-tab total for the location based on the input contextual data corresponding to the location;

a report generator to, when the estimated in-tab total is below a threshold, generate a report including the estimated in-tab total; and

the interface to transmit the report.

5 . The apparatus of claim 4 , wherein the filter is to determine an actual in-tab total for the location, the report generator to compare the actual in-tab total to the estimated in-tab total.

6 . The apparatus of claim 5 , further including a problem mitigator to identify a technical issue with a meter of the meters when the estimated in-tab total is lower than the actual in-tab total.

7 . The apparatus of claim 5 , further including a problem mitigator to mitigate a technical issue with a meter of the meters when the estimated in-tab total is lower than the actual in-tab total.

8 . The apparatus of claim 4 , further including an explainability determiner to determine explainability information identifying a factor that the model relied on in determining the estimation.

9 . A non-transitory computer readable storage medium comprising instructions which, when executed, cause one or more processors to at least:

obtain (A) contextual data obtained from servers and (B) validated in-tab totals, the validated in-tab totals corresponding to a number of meters in a location that have transmitted metering data within a threshold duration of time;

filter at least one of the contextual data based on the location; and

train a model using filtered contextual data and the validated in-tab totals, the trained model to estimate an in-tab total for the location based on input contextual data corresponding to the location.

10 . The computer readable storage medium of claim 9 , wherein the contextual data includes that that corresponds to information that may result in a meter dropping out-of-tab.

11 . The computer readable storage medium of claim 9 , wherein the threshold duration of time is a first threshold duration of time, the contextual data corresponding to a second threshold duration of time from when the validated in-tab totals were obtained.

12 . The computer readable storage medium of claim 9 , wherein the instructions, when executed, cause the one or more processors to:

implement the model to estimate the in-tab total for the location based on the input contextual data corresponding to the location;

in response to the estimated in-tab total being below a threshold, generate a report including the estimated in-tab total; and

transmit the report.

13 . The computer readable storage medium of claim 12 , wherein the instructions cause the one or more processors to:

determine an actual in-tab total for the location; and

compare the actual in-tab total to the estimated in-tab total.

14 . The computer readable storage medium of claim 13 , wherein the instructions cause the one or more processors to identify a technical issue with a meter of the meters when the estimated in-tab total is lower than the actual in-tab total.

15 . The computer readable storage medium of claim 13 , wherein the instructions cause the one or more processors to mitigate a technical issue with a meter of the meters when the estimated in-tab total is lower than the actual in-tab total.

16 . The computer readable storage medium of claim 12 , wherein the instructions cause the one or more processors to determine explainability information identifying a factor that the model relied on in determining the estimation.

17 . A method comprising:

obtaining (A) contextual data obtained from servers and (B) validated in-tab totals, the validated in-tab totals corresponding to a number of meters in a location that have transmitted metering data within a threshold duration of time;

filtering at least one of the contextual data based on the location; and

training a model using filtered contextual data and the validated in-tab totals, the trained model to estimate an in-tab total for the location based on input contextual data corresponding to the location.

18 . The method of claim 17 , wherein the contextual data includes that that corresponds to information that may result in a meter dropping out-of-tab.

19 . The method of claim 17 , wherein the threshold duration of time is a first threshold duration of time, the contextual data corresponding to a second threshold duration of time from when the validated in-tab totals were obtained.

20 . The method of claim 17 , further including:

implementing the model to estimate the in-tab total for the location based on the input contextual data corresponding to the location;

when the estimated in-tab total is below a threshold, generating a report including the estimated in-tab total; and

transmitting the report.