METHODS AND APPARATUS TO EXPLAIN A TREND IN MULTI-DIMENSIONAL TIME-SERIES DATA
Systems, apparatus, articles of manufacture, and methods to explain a trend in multi-dimensional time-series data are disclosed. An example method includes detecting a trend in multi-variate time-series data, providing data associated with the detected trend to a first machine learning model to cause summarization of the detected trend, and providing the summarization of the detected trend to a second machine learning model to cause preparation of an explanation of the summarization, the explanation of the summarization based on one or more documents made available to the second machine learning model.
1 . At least one non-transitory machine-readable medium comprising machine-readable instructions to cause at least one processor circuit to at least:
detect a trend in multi-dimensional time-series data;
provide data associated with the detected trend to a first machine learning model to cause summarization of the detected trend; and
provide the summarization of the detected trend to a second machine learning model to cause preparation of an explanation of the summarization, the explanation of the summarization based on one or more documents made available to the second machine learning model.
2 . The at least one non-transitory computer readable medium of claim 1 , wherein to provide the data associated with the detected trend to the first machine learning model, the machine-readable instructions are to cause one or more of the at least one processor circuit to prepare a first prompt to be provided to the first machine learning model.
3 . The at least one non-transitory computer readable medium of claim 2 , wherein to provide the summarization of the detected trend to the second machine learning model, the machine-readable instructions are to cause one or more of the at least one processor circuit to prepare a second prompt to be provided to the second machine learning model.
4 . The at least one non-transitory computer readable medium of claim 1 , wherein the one or more documents are made available to the second machine learning model using retrieval augmented generation.
5 . The at least one non-transitory computer readable medium of claim 1 , wherein to detect the trend, one or more of the at least one processor circuit is to utilize one or more decision trees.
6 . The at least one non-transitory computer readable medium of claim 5 , wherein the utilization of the one or more decision trees is based on a plurality of dimensions used when building the decision tree.
7 . The at least one non-transitory computer readable medium of claim 6 , wherein the one or more decision trees includes a first decision tree and a second decision tree, the first decision tree utilizing the plurality of dimensions arranged in a first order, and the second decision tree utilizing the plurality of dimensions arranged in a second order different from the first order.
8 . An apparatus comprising:
interface circuitry;
machine-readable instructions; and
at least one processor circuit to be programmed by the machine-readable instructions to:
detect a trend in multi-dimensional time-series data;
provide data associated with the detected trend to a first machine learning model to cause summarization of the detected trend; and
provide the summarization of the detected trend to a second machine learning model to cause preparation of an explanation of the summarization, the explanation of the summarization based on one or more documents made available to the second machine learning model.
9 . The apparatus of claim 8 , wherein to provide the data associated with the detected trend to the first machine learning model, one or more of the at least one processor circuit is to prepare a first prompt to be provided to the first machine learning model.
10 . The apparatus of claim 9 , wherein to provide the summarization of the detected trend to the second machine learning model, one or more of the at least one processor circuit is to prepare a second prompt to be provided to the second machine learning model.
11 . The apparatus of claim 8 , wherein the one or more documents are made available to the second machine learning model using retrieval augmented generation.
12 . The apparatus of claim 8 , wherein to detect the trend, one or more of the at least one processor circuit is to utilize one or more decision trees that are based on a plurality of dimensions used when building the decision tree.
13 . The apparatus of claim 12 , wherein the one or more decision trees includes a first decision tree and a second decision tree, the first decision tree utilizing the plurality of dimensions arranged in a first order, and the second decision tree utilizing the plurality of dimensions arranged in a second order different from the first order.
14 . A method comprising:
detecting a trend in multi-variate time-series data;
providing data associated with the detected trend to a first machine learning model to cause summarization of the detected trend; and
providing the summarization of the detected trend to a second machine learning model to cause preparation of an explanation of the summarization, the explanation of the summarization based on one or more documents made available to the second machine learning model.
15 . The method of claim 14 , wherein to provide the data associated with the detected trend to the first machine learning model, the method further includes preparing a first prompt to be provided to the first machine learning model.
16 . The method of claim 15 , wherein to provide the summarization of the detected trend to the second machine learning model, the method further includes preparing a second prompt to be provided to the second machine learning model.
17 . The method of claim 14 , wherein the one or more documents are made available to the second machine learning model using retrieval augmented generation.
18 . The method of claim 14 , wherein the trend is detected using one or more decision trees.
19 . The method of claim 18 , wherein the use of the one or more decision trees is based on a plurality of dimensions used when building the decision tree.
20 . The method of claim 19 , wherein the one or more decision trees includes a first decision tree and a second decision tree, the first decision tree utilizing the plurality of dimensions arranged in a first order, and the second decision tree utilizing the plurality of dimensions arranged in a second order different from the first order.