Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/139704
Title: FUNCTIONAL MACHINE LEARNING WITH APPLICATION TO DAY-AHEAD FORECASTING OF NATURAL GAS FLOW DATA
Authors: XU XIUQIN
Keywords: functional data, LSTM, time series, machine learning, energy, FPCA
Issue Date: 28-Dec-2017
Source: XU XIUQIN (2017-12-28). FUNCTIONAL MACHINE LEARNING WITH APPLICATION TO DAY-AHEAD FORECASTING OF NATURAL GAS FLOW DATA. ScholarBank@NUS Repository.
Abstract: In this thesis, we propose Long Short-Term Memory (LSTM) model for forecasting functional time series for which the underlying data are defined as functions in continuous space. The functional data can be decomposed based on either fixed parametric basis functions through the Fourier expansion or data-based basis functions through functional principal component analysis (FPCA). In our proposed model, the LSTM is implemented based on the Fourier expansion coefficients or principal components scores of the functional data. The functional machine learning techniques are applied to real-world data, the hourly natural gas flow in Germany and compared with two alternative models – Persistence model and the LSTM directly applied to the original discrete data. We found that the Fourier based LSTM has slightly better prediction accuracy compared with the Persistence model and the original data based LSTM model. However, the FPCA based LSTM does not perform well in our case.
URI: http://scholarbank.nus.edu.sg/handle/10635/139704
Appears in Collections:Master's Theses (Restricted)

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