Abstract: A method and a system for using a learnable augmentation technique to perform few-shot calibration of a model that is designed to generate time series predictions under distribution shifts with improved accuracy are provided. The method includes: receiving first information that relates to a source distribution of a time series and second information that relates to a target distribution of the time series; extracting a latent code from samples of the target distribution; perturbing the latent code by adding random noise in order to generate augmented samples of the target distribution; training a classifier based on samples of the source distribution; adjusting the classifier based on a combination of the samples of the source distribution and the augmented samples of the target distribution; and using the adjusted classifier to train a machine learning model that is usable for making future predictions that relate to the time series.

Abstract: A method for using a Gaussian Process-based algorithm to approximate an optimal stopping of a time series that corresponds to a sequence of events is provided. The method includes: receiving information that relates to an event sequence; estimating, based on the received information, a first potential reward that is obtained by stopping the event sequence at a first time, and a set of respective second potential rewards that are obtained by stopping the event sequence at corresponding times; and determining, based on the estimated first and second potential rewards, an optimal time for stopping the event sequence. The event sequence may include a numerical sequence that is modeled as a statistical learning method via a Gaussian Process (GP) function and/or a deep GP function that indicates a probability density distribution of the items in the numerical sequence over a predetermined time interval.

Abstract: A method and a system for using implicit neural representations for generation of interpretable time series are provided. The method includes: receiving time series information, such as pairings of time coordinate values with time series signal values, that relates to an event sequence; generating, based on the time series information, an implicit neural representation of the event sequence that includes a plurality of embedded values and a corresponding plurality of weights; and using the implicit neural representation to predict at least one item of information that relates to the event sequence and is not included in the received time series information, such as an interpolation or an extrapolation of the time series.

Abstract: A method for using an artificial intelligence (AI) model to simulate a limit order book market in order to facilitate study and evaluation of trading strategies is provided. The method includes: receiving information that relates to a state of the market at a particular time; and determining, based on the information, a potential market action that is expected to occur. The determination is made by applying an AI algorithm that implements a machine learning technique to determine the potential market action. The AI algorithm is trained by using historical data that relates to the market.

Abstract: Error correction coding for streaming communication is provided. A streaming problem is modeled as a non-multicast network problem with a nested receiver structure. Each packet in the streaming problem corresponds to a link, and each deadline in the streaming problem corresponds to a receiver in the non-multicast network problem. For the non-multicast network problem, content to be transmitted in multiple packets to multiple receivers is obtained. Each of the receivers is required to decode specific independent messages from the content, at given time steps, and has access to a subset of the content received by another receiver. The content is allocated into multiple packets to be transmitted on multiple links. No coding occurs across information demanded by different receivers. A capacity region defines a set of information rate vectors that can be communicated to the receivers successfully. A rate vector is successfully communicated if it complies with various inequalities.