Sequence prediction using recurrent neural network - Nguyen, Phan Huy (2017)
The project implemented a Gap-Filling Engine capable of filling in gaps in missing sequences of various types. A strategy was introduced to look forward into subsequent data, enabling the Engine to improve the accuracy of the prediction by more than 30%. A Sequence Model based on Long Short-term Memory (LSTM) Recurrent Neural Network was used to learn patterns in several sequence types. Optimization technique by caching the LSTM was implemented, shortening runtime by several hundred times, allowing significantly more experiments to be executed. Performance evaluation strategy was designed and carried out to analyze the impact of various factors on the Gap-Filling Engine, providing a better understand of the Engine, as well as recommendations for users. The Gap-Filling Engine shows potentials of applications on several fields, especially in reconstructing structured sequential data with missing values.