Sequence prediction using recurrent neural network

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 Mem...

Full description

Saved in:
Bibliographic Details
Main Author: Nguyen, Phan Huy
Other Authors: Goh Wooi Boon
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/70503
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-70503
record_format dspace
spelling sg-ntu-dr.10356-705032023-03-03T20:41:52Z Sequence prediction using recurrent neural network Nguyen, Phan Huy Goh Wooi Boon School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering 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. Bachelor of Engineering (Computer Science) 2017-04-26T01:41:25Z 2017-04-26T01:41:25Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70503 en Nanyang Technological University 77 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Nguyen, Phan Huy
Sequence prediction using recurrent neural network
description 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.
author2 Goh Wooi Boon
author_facet Goh Wooi Boon
Nguyen, Phan Huy
format Final Year Project
author Nguyen, Phan Huy
author_sort Nguyen, Phan Huy
title Sequence prediction using recurrent neural network
title_short Sequence prediction using recurrent neural network
title_full Sequence prediction using recurrent neural network
title_fullStr Sequence prediction using recurrent neural network
title_full_unstemmed Sequence prediction using recurrent neural network
title_sort sequence prediction using recurrent neural network
publishDate 2017
url http://hdl.handle.net/10356/70503
_version_ 1759856639743098880