Systems, methods, and apparatus for time series data adaptation, including sensor fusion, are disclosed. For example, a system includes a variational inference machine, a sequential data forecast machine including a hidden state, and a machine learning model. The sequential data forecast machine exports a version of the hidden state. The variational inference machine receives as inputs time series data and the version of the hidden state, and outputs a time dependency infused latent distribution. The sequential data forecast machine obtains the version of the hidden state, receives as inputs the time series data and the time dependency infused latent distribution, and updates the hidden state based on the time series data, the time dependency infused latent distribution, and the version of the hidden state to generate a second version of the hidden state. The time dependency infused latent distribution is input into the machine learning model, which outputs a result.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a variational inference machine that includes a first input layer, a first plurality of hidden layers, and a first output layer; a sequential data forecast machine that includes a second input layer and a second plurality of hidden layers, the second plurality of hidden layers including a hidden state; and a machine learning model, wherein the variational inference machine, the sequential data forecast machine, and the machine learning model are configured to iteratively perform: exporting, from the second plurality of hidden layers, a first version of the hidden state, which includes time dependency information, receiving, at the first input layer, (i) time series data of a first time interval from at least a first sensor and (ii) the first version of the hidden state, outputting, by the first output layer, a time dependency infused latent distribution that is generated based on the time series data of the first time interval and the first version of the hidden state, obtaining, by the second plurality of hidden layers, the first version of the hidden state, receiving, at the second input layer, (i) the time series data of the first time interval and (ii) the time dependency infused latent distribution from the variational inference machine, updating the hidden state in the second plurality of hidden layers based on the time series data of the first time interval, the time dependency infused latent distribution, and the first version of the hidden state to generate a second version of the hidden state, inputting the time dependency infused latent distribution into the machine learning model, and outputting, from the machine learning model, a result of the first time interval based on the time dependency infused latent distribution. 2. The system of claim 1, wherein the result causes an instruction to be sent to an actuator to perform an actuation. 3. The system of claim 2, wherein the actuation is at least one of steering and braking. 4. The system of claim 1, wherein the time series data, which is output from a first sensor, is unavailable at the first time interval. 5. The system of claim 4, wherein the result is fault tolerant to intermittent unavailability of the first sensor at the first time interval. 6. The system of claim 1, wherein the time series data is multi-dimensional data from one of a an image sensor, a lidar sensor, and a radar sensor. 7. The system of claim 1, wherein the time series data includes a first time series of a first modality and a second time series of a second modality, and the time dependency infused latent distribution is a multi-modal time dependency infused latent distribution. 8. The system of claim 1, further comprising a first sensor and a second sensor, wherein the first sensor senses a first type of data of a first modality, and the second sensor senses a second type of data of a second modality. 9. The system of claim 8, wherein the first modality is radar and the second modality is ultrasound. 10. The system of claim 8, further comprising a third sensor, which senses a third type of data of a third modality. 11. The system of claim 10, wherein the first modality is radar, the second modality is ultrasound, and the third modality is lidar. 12. The system of claim 1, wherein the first input layer and the second input layer receive a first version of a second hidden state from a second sequential data forecast machine. 13. The system of claim 12, wherein time dependency infused latent distribution is generated based on the first version of the second hidden state from the second sequential data forecast machine. 14. The system of claim 13, wherein the second sequential data forecast machine is a multi-modal sequential data forecast machine. 15. The system of claim 1, wherein the first input layer includes a first sublayer and a second sublayer, wherein the time series data is input into the first sublayer, and the hidden state is input into the second sublayer. 16. The system of claim 1, wherein the sequential data forecast machine includes a recurrent neural network. 17. The system of claim 16, wherein the recurrent neural network includes a second output layer that outputs a prediction of future time series data. 18. The system of claim 1, further comprising a variational generation machine that generates an output representative of the time series data received by the first input layer. 19. The system of claim 1, wherein the system is implemented in an edge device. 20. The system of claim 1, wherein the system is implemented in a vehicle. 21. The system of claim 1, wherein a new version of the hidden state is generated in a different time interval at least once every 30 milliseconds. 22. The system of claim 21, wherein the machine learning model executes at least once every 30 milliseconds using a new version of the time dependency infused latent distribution. 23. The system of claim 1, wherein the time series data includes image data. 24. The system of claim 1, wherein the time series data includes sound data. 25. The system of claim 1, wherein the time series data includes acceleration data. 26. The system of claim 1, wherein the time series data is binary data. 27. The system of claim 1, wherein the result is at least one of a forecast, a prediction, a classification, a clustering, an anomaly detection, and a recognition. 28. A method comprising: exporting, from a plurality of hidden layers of a sequential data forecast machine, a first version of a hidden state that includes time dependency information; receiving, at a first input layer of a variational inference machine, (i) time series data of a first time interval from at least a first sensor and (ii) the first version of the hidden state; outputting, by a first output layer of the variational inference machine, a time dependency infused latent distribution that is generated based on the time series data of the first time interval and the first version of the hidden state; obtaining, by the plurality of hidden layers, the first version of the hidden state; receiving, at a second input layer of the sequential data forecast machine, (i) the time series data of the first time interval and (ii) the time dependency infused latent distribution from the variational inference machine; updating the hidden state in the plurality of hidden layers based on the time series data of the first time interval, the time dependency infused latent distribution, and the first version of the hidden state to generate a second version of the hidden state; inputting the time dependency infused latent distribution into the machine learning model; and outputting, from the machine learning model, a result of the first time interval based on the time dependency infused latent distribution. 29. A system comprising: a multi-modal variational inference machine that includes a first input layer, a first plurality of hidden layers, and a first output layer; and a multi-modal sequential data forecast machine that includes a second input layer and a second plurality of hidden layers, the second plurality of hidden layers including a hidden state, wherein the multi-modal variational inference machine and the multi-modal sequential data forecast machine are configured to iteratively perform: exporting, from the second plurality of hidden layers, a first version of the hidden state, which includes time dependency information, receiving, at the first input layer, (i) a first time dependency infused latent distribution of a first time interval from a first variational inference machine, (ii) a second time dependency infused latent distribution of the first time interval from a second variational inference machine, and (iii) the first version of the hidden state, outputting, by the first output layer, a multi-modal time dependency infused latent distribution of the first time interval that is generated based on the first time dependency infused latent distribution of the first time interval, the second time dependency infused latent distribution of the first time interval, and the first version of the hidden state, obtaining, by the second plurality of hidden layers, the first version of the hidden state, receiving, at the second input layer, (i) the first time dependency infused latent distribution of the first time interval, (ii) the second time dependency infused latent distribution of the first time interval, and (iii) the multi-modal time dependency infused latent distribution of the first time interval from the multi-modal variational inference machine, and updating the hidden state in the second plurality of hidden layers based on the first time dependency infused latent distribution of the first time interval, the second time dependency infused latent distribution of the first time interval, the multi-modal time dependency infused latent distribution of the first time interval, and the first version of the hidden state to generate a second version of the hidden state. 30. The system of claim 29, further comprising a machine learning model that is configured to: input the multi-modal time dependency infused latent distribution of the first time interval into the machine learning model, and output, from the machine learning model, a result of the first time interval based on the multi-modal time dependency infused latent distribution of the first time interval. 31. The system of claim 29, wherein the first version of the hidden state is provided to at least: a first variational inference machine and a first sequential data forecast machine, which generate the first time dependency infused latent distribution of the first time interval, and a second variational inference machine and a second sequential data forecast machine, which generate the first second dependency infused latent distribution of the second time interval. 32. A method comprising: exporting, from a plurality of hidden layers of a multi-modal sequential data forecast machine, a first version of a hidden state that includes time dependency information; receiving, at a first input layer of a multi-modal variational inference machine, (i) a first time dependency infused latent distribution of a first time interval from a first variational inference machine, (ii) a second time dependency infused latent distribution of the first time interval from a second variational inference machine, and (iii) the first version of the hidden state outputting, by a first output layer of the multi-modal variational inference machine, a multi-modal time dependency infused latent distribution that is generated based on the first time dependency infused latent distribution of a first time interval, the second time dependency infused latent distribution of the first time interval, and the first version of the hidden state; obtaining, by the plurality of hidden layers, the first version of the hidden state; receiving, at a second input layer of the multi-modal sequential data forecast machine, (i) the first time dependency infused latent distribution of the first time interval, (ii) the second time dependency infused latent distribution of the first time interval, and (iii) the multi-modal time dependency infused latent distribution of the first time interval from the multi-modal variational inference machine; updating the hidden state in the plurality of hidden layers based on the first time dependency infused latent distribution of the first time interval, the second time dependency infused latent distribution of the first time interval, the multi-modal time dependency infused latent distribution of the first time interval, and the first version of the hidden state to generate a second version of the hidden state.