Time Series Forecasting with Recurrent Neural Networks in Presence of Missing Data
Permanent link
https://hdl.handle.net/10037/14887Date
2018-11-24Type
Master thesisMastergradsoppgave
Author
Choi, ChangkyuAbstract
In many applications, time series forecasting plays an irreplaceable role in time-varying systems such as energy markets, financial markets, and so on.
Predicting the dynamic of time-varying systems is essential but is a difficult task because it depends on not only the nature of the system but also on external influences, such as environmental conditions and social and economic status.
Recurrent Neural Networks (RNNs) are a special class of neural networks characterized by the recurrent internal connections, which enable to model the nonlinear dynamical system.
Recently, they have been applied in the various forecasting tasks and reported that they outperform the forecast accuracy compared with conventional time series forecasting models.
However, there is a limited study of time series forecasting using RNNs in the presence of missing data.
In this thesis, we propose a novel model that utilize Dilated RNN(DRNN) and a modified attention mechanism, focusing on the problem of time series forecasting with missing data.
The proposed model outperforms existing models such as AutoRegressive Integrated Moving Average(ARIMA) and Gated Recurrent Unit(GRU), with respect to the forecast accuracy on benchmark datasets.
Besides, we provide a formal description of the learning procedure of RNNs, referred as truncated BPTT(k2, k1), and explain how to construct mini-batches of the training dataset for the forecasting tasks with RNNs, that has not been presented before this work.
Discussions and future directions are suggested in five different perspectives at the end.
Publisher
UiT Norges arktiske universitetUiT The Arctic University of Norway
Metadata
Show full item recordCollections
Copyright 2018 The Author(s)
The following license file are associated with this item:
Related items
Showing items related by title, author, creator and subject.
-
Implementing an electronic health record in a Nigerian secondary healthcare facility. Prospects and challenges
Attah, Ambrose Ojadale (Master thesis; Mastergradsoppgave, 2017-11-02)Nigeria is witnessing continuing advocacy and increase in number of individuals yearning for computerization of health information and healthcare processes. However, little is known about the opinions of the diverse healthcare providers who would ensure the successful implementation and meaningful use of health information technology in the country (Adeleke, Erinle et al. 2015). This study explores ... -
Geometric Modeling- and Sensor Technology Applications for Engineering Problems
Pedersen, Aleksander (Doctoral thesis; Doktorgradsavhandling, 2020-10-20)In applications for technical problems, Geometric modeling and sensor technology are key in both scientific and industrial development. Simulations and visualization techniques are the next step after defining geometry models and data types. This thesis attempts to combine different aspects of geometric modeling and sensor technology as well as to facilitate simulation and visualization. It includes ... -
Cogset : A High-Performance MapReduce Engine
Viken Valvåg, Steffen (Doctoral thesis; Doktorgradsavhandling, 2012-01-30)MapReduce has become a widely employed programming model for large-scale data-intensive computations. Traditional MapReduce engines employ dynamic routing of data as a core mechanism for fault tolerance and load balancing. An alternative mechanism is static routing, which reduces the need to store temporary copies of intermediate data, but requires a tighter coupling between the components for ...