SYSTEMS AND METHODS FOR MODEL-BASED TIME SERIES ANALYSIS

1 . A system comprising:

a computing device comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:

execute a model for analyzing a time series of data;

receive a labeled time series of data including a plurality of variables at a plurality of points in time;

analyze the labeled time series of data;

generate a causal graph of an event based on the analysis;

calculate a predicted value for one or more variables of the plurality of variables at a specific point in time;

compare the predicted value to an observed value for the one or more variables; and

adjust the model based on the comparison.

2 . The system of claim 1 , wherein the labeled time series of data includes at least one label and at least one event, and wherein the at least one label precedes the at least one event.

3 . The system of claim 1 , wherein the at least one processor is further programmed to generate a class for the label and the corresponding event.

4 . The system of claim 1 , wherein the at least one processor is further programmed to analyze the labeled time series of data with a plurality of Gated Recurrent Units (GRUs).

5 . The system of claim 4 , wherein the plurality of GRUs are modified to project data into a temporal embedding space.

6 . The system of claim 4 , wherein the plurality of GRUs include a plurality of layers of GRUs.

7 . The system of claim 6 , wherein the at least one processor is further programmed to utilize results from each layer of GRU to generate the causal graph.

8 . The system of claim 1 , wherein the at least one processor is further programmed to generate a plurality of linear combinations based on the causal graph and the labeled time series of data.

9 . The system of claim 1 , wherein the model is adjusted to detect the event based on the time series of data.

10 . The system of claim 1 , wherein the at least one processor is further programmed to:

receive a plurality of different labeled time series of data; and

adjust the model based on the analysis of each of the plurality of different labeled time series of data.

11 . A system comprising:

a computing device comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:

execute a model for analyzing a time series of data, wherein the model includes a plurality of classes;

receive an unlabeled time series of data including a plurality of variables at a plurality of points in time;

analyze the unlabeled time series of data;

compare the analyzed data to the plurality of classes;

for each class of the plurality of classes, calculate a predicted value for one or more variables of the plurality of variables at a specific point in time;

compare the plurality of predicted values to an observed value for the one or more variables; and

assign a label to the time series of data based on the comparison.

12 . The system of claim 11 , wherein the unlabeled time series of data is based on sensor data of a device.

13 . The system of claim 11 , wherein the at least one processor is further programmed to adjust performance of a device associated with the time series of data based on the label.

14 . The system of claim 11 , wherein the at least one processor is further programmed to analyze the unlabeled time series of data with a plurality of Gated Recurrent Units (GRUs).

15 . The system of claim 14 , wherein the plurality of GRUs are modified to project data into temporal embedding space.

16 . The system of claim 14 , wherein the plurality of GRUs include a plurality of layers of GRUs.

17 . A method for detecting an event, the method implemented by a computing device including at least one processor in communication with at least one memory device, the method comprising:

executing a model for analyzing a time series of data, wherein the model includes a plurality of classes;

receiving an unlabeled time series of data including a plurality of variables at a plurality of points in time;

analyzing the unlabeled time series of data;

comparing the analyzed data to the plurality of classes;

for each class, calculating a predicted value for one or more variables of the plurality of variables at a specific point in time;

comparing the predicted value to an observed value for the one or more variables; and

assigning a label to the time series of data based on the comparison.

18 . The method of claim 17 , wherein the unlabeled time series of data is based on sensor data of a device, and wherein the method further comprises adjusting performance of a device associated with the time series of data based on the label.

19 . The method of claim 17 further comprising analyzing the unlabeled time series of data with a plurality of Gated Recurrent Units (GRUs), wherein the plurality of GRUs are modified to project data into temporal embedding space, and wherein the plurality of GRUs include a plurality of layers of GRUs.

20 . The method of claim 17 further comprising calculating the predicted value for each class of the plurality of classes, wherein each class is associated with a type of event and wherein the label is associated with the type of event detected.