Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150279
Title: Improving time series forecasting performance with deep learning techniques
Authors: Zhang, Kanghao
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Zhang, K. (2021). Improving time series forecasting performance with deep learning techniques. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150279
Abstract: Time series are ubiquitous in nature and human society. Especially, the forecasting of time series could be instructive in meteorology, commerce, energy management, financial activities, and various fields. Hence, time series forecasting is the most important task under the topic of time series analysis. Many models have been proved to be effective for time series forecasting such as ARIMA, LSTM, TCN, RNN, and RVFL. This dissertation is intended to improve the accuracy of time series forecasting by using TCN-based algorithms. In this dissertation, three novel hybrid algorithms: TCN-edRVFL algorithm, multi-receptive field TCN (MRF-TCN algorithm), and dynamic decision multi-receptive field TCN (DDM-TCN algorithm) are proposed. At last, experiments of electric load forecasting are implemented on Australian Energy Market Operator (AEMO) datasets. Comparisons and results analysis indicate that the proposed algorithms outperform the TCN baseline in different aspects.
URI: https://hdl.handle.net/10356/150279
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Zhang_Kanghao_MSc_Thesis.pdf
  Restricted Access
2.86 MBAdobe PDFView/Open

Page view(s)

86
Updated on Jan 23, 2022

Download(s)

3
Updated on Jan 23, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.