RECURRENT NEURAL NETWORK BASED PREDICTIONS

Systems and methods for predicting a future status of an entity using a recurrent neural network are described. A system is configured to obtain a first time series data associated with a first entity. The system is also configured to predict, by a recurrent neural network, a future equity status of...

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Bibliographische Detailangaben
Hauptverfasser:	TAYEB, Yaaqov, BECHLER, Sigalit, ZICHAREVICH, Alexander
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:	Systems and methods for predicting a future status of an entity using a recurrent neural network are described. A system is configured to obtain a first time series data associated with a first entity. The system is also configured to predict, by a recurrent neural network, a future equity status of the first entity based on the time series data. The system is also configured to provide an indication of the future equity status of the first entity, with the future equity status of the first entity to be indicated to a user. An equity status may be a cash status of an entity, and predicting the cash status of the first entity can be based on time series of a plurality of features associated with the first entity provided to the recurrent neural network. The recurrent neural network may be a long short-term memory network.