Abstract: An ensemble time series prediction system that makes predictions based on observed data. The disclosed ensemble time series prediction system may leverage different types of datasets and information from different resources for making predictions. The disclosed ensemble time series prediction system may extract time dependent features from autoregressive time dependent data, embedding features from sparse datasets, continuous features from continuous dataset, and time lagged features from data that include time-lag information. The disclosed ensemble time series prediction system may then consolidate the features extracted from the different types of datasets and generate a set of consolidated input features for training a neural network, which may include a recurrent neural unit that finds sequential pattern for the sequence of input features and a regression unit that performs regression and predictions.

Abstract: A system according determines a machine learning based model for forecasting time series data for a given use case. The system determines a model metric for a specific use case of time series data. The system accesses a pool of machine learning based models including a plurality of machine learning based models machine learning based models based on different machine learning techniques. For each of the plurality of machine learning based models the system performs forecasting using the machine learning based model and determines the value of the model metric for the machine learning based model. The system selects a machine learning based model based on comparison of values of the model metric for machine learning based models. The system uses the selected machine learning based model for forecasting values for the time series data for the application.

Abstract: A system according to an embodiment optimizes communications with users using machine learning based models. The system receives user profile data for a set of users. For each user from the set of users, the system provides the user profile data as input to a machine learning based model and determines attributes describing the user, for example a measure of adherence rate for the user. The system ranks the set of users based on the predicted attributes. The system selects a subset of users from the set of users based on the ranking. For each selected user from the set of selected users, the system determines communication parameters for communicating with the selected user and sends a communication to the selected user based on the determined communication parameters.

Abstract: A system trains a neural network for predicting time for communicating with users. The system trains the neural network using user data for users that includes a communication time series and an event time series. The system trains the neural network by masking a portion of the time series data and provides the masked time series data as input to the neural network. The system executes the neural network to predict values of the masked portion of the time series data. The system determines a loss value based on the accuracy of the prediction of the masked portion of the time series data and adjusts parameters of the neural network to minimize the loss value. The system uses the trained neural network to predict timing for communicating with a particular user.

Abstract: A system uses a machine learning based model to select a channel for communicating with users. The system generates a feature vector based on a user profile of the user. The user profile data includes time series data describing past communications to users and past user actions. The system executes one or more machine learning based models, each machine learning based model configured to receive a feature vector describing a particular user and predict a likelihood of the particular user performing an expected user action responsive to a communication sent via the communication channel. The system selects a communication channel based on the results of the machine learning based models and sends a communication to the user via the selected communication channel.

Abstract: An apparatus for estimating blood pressure includes: a ballistocardiogram (BCG) sensor configured to measure a limb BCG signal from a user; and a processor configured to obtain blood pressure-related features from the measured limb BCG signal, according to sensing characteristics of the BCG sensor, and estimate a blood pressure of the user based on the obtained blood pressure-related features.

Abstract: An apparatus for generating a blood pressure estimation model includes: a signal acquirer configured to receive input a first signal and a second signal from a user; and a processor configured to obtain a pulse transit time (PTT) as a first predictor variable value based on the first signal and the second signal, to extract at least one feature from the second signal, to obtain a second predictor variable value based on the extracted at least one feature, and to generate a blood pressure estimation model based on the first predictor variable value and the second predictor variable value.

Abstract: An apparatus for estimating bio-information of a user may include a first sensor configured to measure a first signal from the user; a second sensor configured to measure a second signal from the user; and a processor configured to obtain a first characteristic point from the first signal; obtain a second characteristic point from the second signal based on the first characteristic point and a pre-defined probability distribution function; and estimate the bio-information of the user based on the second characteristic point.

Abstract: An apparatus for estimating blood pressure includes: a ballistocardiogram (BCG) sensor configured to measure a limb BCG signal from a user; and a processor configured to obtain blood pressure-related features from the measured limb BCG signal, according to sensing characteristics of the BCG sensor, and estimate a blood pressure of the user based on the obtained blood pressure-related features.

Abstract: An apparatus for measuring blood pressure according to one aspect may include a limb ballistocardiogram (BCG) sensor configured to attach to a limb of a user and measure a limb BCG signal of the user, and a processor configured to extract blood pressure-related features from the measured limb BCG signal and estimate blood pressure of the user based on at least part of the extracted blood pressure-related features.