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Title:Time series forecasting with recurrent neural networks
Author(s):Pan, Zhonghao
Advisor(s):Kim, Harrison M
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Time Series
Recurrent Neural Networks
LSTM
GRU
CEEMDAN
Abstract:Time series, such as demand trends, stock prices, and sensor data, is an essential data type in our modern world. Over the years, many models such as Exponential Smoothing and ARIMA are developed to make forecasts on time series. Recently, Recurrent Neural Networks (RNN) is gaining traction in the field of time series forecasting. RNN is a type of specialized neural network tailored towards handling sequential data such as natural language and time series. RNN models such as LSTM networks and GRU networks are widely used in literature. Besides, different feature engineering methods such as CEEMDAN are also tools employed in the literature to improve prediction accuracy. In this paper, we will introduce different models and methods of handling time series and will conduct a comparative case study using the S$\&$P500 index to compare the effectiveness of these models.
Issue Date:2021-04-18
Type:Thesis
URI:http://hdl.handle.net/2142/110479
Rights Information:Copyright 2021 Zhonghao Pan
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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