Prediction of time series data using AI technique

This paper will be covering AI techniques in the prediction of climate change data over the course of 140 years. We constructed different neural network architectures to evaluate their effectiveness in forecasting time series data. The various neural network being explored are namely Multi-Layered P...

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Main Author: Goh, Mei Hui
Other Authors: Chan Chee Keong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/141438
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1414382023-07-07T18:50:03Z Prediction of time series data using AI technique Goh, Mei Hui Chan Chee Keong School of Electrical and Electronic Engineering Chan Chee Keong ECKCHAN@ntu.edu.sg Engineering::Electrical and electronic engineering This paper will be covering AI techniques in the prediction of climate change data over the course of 140 years. We constructed different neural network architectures to evaluate their effectiveness in forecasting time series data. The various neural network being explored are namely Multi-Layered Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) neural network and a hybrid model which is CNN-LSTM hybrid model. Using time-series feature extraction, this univariate time series problem is transformed into supervised learning problems for which neural networks can be trained on. 70% of the data are split sequentially to form the training data set whilst the 30% of the data are used as test dataset. The metrics that is used to score the models are Root Mean Square Error (RMSE). This FYP report will delve into 2 datasets with one dataset having approximately 2000 rows of data and another bigger dataset with approximately 50,000 rows of data. To increase the accuracy of the models, this paper explores the use of LSTM autoencoders to reconstruct the input space and hyperparameter tuning of the network parameters via grid search. Lastly, the constructed networks are benchmarked across traditional statistical models and machine learning models like random forest, etc in the 2 datasets. It was observed that CNNLSTM hybrid neural network and MLP gives the best result for small dataset whilst linear regression gives the best result for large dataset but in comparison, CNN-LSTM and MLP models are close in accuracy too. Bachelor of Engineering (Information Engineering and Media) 2020-06-08T07:57:20Z 2020-06-08T07:57:20Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141438 en A3040-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Goh, Mei Hui
Prediction of time series data using AI technique
description This paper will be covering AI techniques in the prediction of climate change data over the course of 140 years. We constructed different neural network architectures to evaluate their effectiveness in forecasting time series data. The various neural network being explored are namely Multi-Layered Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) neural network and a hybrid model which is CNN-LSTM hybrid model. Using time-series feature extraction, this univariate time series problem is transformed into supervised learning problems for which neural networks can be trained on. 70% of the data are split sequentially to form the training data set whilst the 30% of the data are used as test dataset. The metrics that is used to score the models are Root Mean Square Error (RMSE). This FYP report will delve into 2 datasets with one dataset having approximately 2000 rows of data and another bigger dataset with approximately 50,000 rows of data. To increase the accuracy of the models, this paper explores the use of LSTM autoencoders to reconstruct the input space and hyperparameter tuning of the network parameters via grid search. Lastly, the constructed networks are benchmarked across traditional statistical models and machine learning models like random forest, etc in the 2 datasets. It was observed that CNNLSTM hybrid neural network and MLP gives the best result for small dataset whilst linear regression gives the best result for large dataset but in comparison, CNN-LSTM and MLP models are close in accuracy too.
author2 Chan Chee Keong
author_facet Chan Chee Keong
Goh, Mei Hui
format Final Year Project
author Goh, Mei Hui
author_sort Goh, Mei Hui
title Prediction of time series data using AI technique
title_short Prediction of time series data using AI technique
title_full Prediction of time series data using AI technique
title_fullStr Prediction of time series data using AI technique
title_full_unstemmed Prediction of time series data using AI technique
title_sort prediction of time series data using ai technique
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/141438
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