Method and system for optimal stopping using fast probabilistic learning algorithms

A computing apparatus for determining an optimal time for stopping an event sequence, the computing apparatus comprising:

• a processor;
• a memory;
• and
• a communication interface coupled to each of the processor and the memory,
• wherein the processor is configured to:
• receive, via the communication interface, first information that relates to the event sequence, wherein the event sequence includes a sequence of values that is modeled by using a Gaussian Process (GP) function that indicates a probability density distribution of the values over a predetermined time interval;
• estimate, based on the first information, a first potential reward that is obtained by stopping the event sequence at a first predetermined time;
• estimate, based on the first information, a plurality of respective second potential rewards that are obtained by stopping the event sequence at a corresponding one from among a plurality of second predetermined times;
• and
• determine, based on the estimated first potential reward and the plurality of estimated second potential rewards, an optimal time for stopping the event sequence,
• wherein the determination of the optimal time for stopping the event sequence comprises applying a non-adaptive artificial intelligence (AI) algorithm that implements a machine learning technique with respect to the first information and the GP function, the non-adaptive AI algorithm being trained by using historical data and fixed warm start data that relates to the event sequence,
• wherein the historical data used in training the non-adaptive AI algorithm are clustered and GP function is fitted to a centroid of each cluster for reducing computational complexity, and
• wherein the fixed warm start data is provided by clustering an initial warm start period of data that relates to the event sequence for reducing computational complexity.

A non-transitory computer readable storage medium storing instructions for determining an optimal time for stopping an event sequence, the storage medium comprising executable code which, when executed by a processor, causes the processor to:

• receive first information that relates to the event sequence;
• estimate, based on the first information, a first potential reward that is obtained by stopping the event sequence at a first predetermined time;
• estimate, based on the first information, a plurality of respective second potential rewards that are obtained by stopping the event sequence at a corresponding one from among a plurality of second predetermined times;
• and
• determine, based on the estimated first potential reward and the plurality of estimated second potential rewards, an optimal time for stopping the event sequence,
• wherein the determination of the optimal time for stopping the event sequence comprises applying a non-adaptive artificial intelligence (AI) algorithm that implements a machine learning technique with respect to the first information and the GP function, the non-adaptive AI algorithm being trained by using historical data and fixed warm start data that relates to the event sequence,
• wherein the historical data used in training the non-adaptive AI algorithm are clustered and GP function is fitted to a centroid of each cluster for reducing computational complexity, and
• wherein the fixed warm start data is provided by clustering an initial warm start period of data that relates to the event sequence for reducing computational complexity.