Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets

Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets

Bibliographic Details
Main Author: Zeng, Jianfeng
Language:English
Published: University of Cincinnati / OhioLINK 2021
Subjects:
Computer Science
Matrix Factorization
Time Series Forecasting
Temporal Regularized Matrix Factorization
Graph Regularization
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617109307510099
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin16171093075100992021-10-02T05:10:33Z Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets Zeng, Jianfeng Computer Science Matrix Factorization Time Series Forecasting Temporal Regularized Matrix Factorization Graph Regularization As technology has advanced, the time series data collected has become larger and larger in size. The problems of time series forecasting have thus become high dimensional. A recent study on time series forecasting attempted to tackle the problem using temporal regularized matrix factorization (TRMF) (Yu, Rao and Dhillon, 2016). In this research, a method is proposed to facilitate the application of time series forecasting using TRMF and a list of other algorithms on traffic speed data. The application is improved in several ways. First, a graph regularization is incorporated into TRMF to utilize spatial dependencies in data. This can improve the forecasting accuracy when strong spatial dependencies are present in data. Second, in order to incorporate temporal dependencies in data, a lag set has to be defined in advance. This would require the user to have both domain knowledge and familiarity with the data. This method can define a lag set algorithmically even with data containing missing values. Third, the forecasting result is improved by using imputed data when missing value rate is high or data is lacking, or damaged. Finally, a multi-step forecasting strategy is used to improve the accuracy of results for long-term forecasting. 2021-09-30 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617109307510099 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617109307510099 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.
collection NDLTD
language English
sources NDLTD
topic Computer Science
Matrix Factorization
Time Series Forecasting
Temporal Regularized Matrix Factorization
Graph Regularization
spellingShingle Computer Science
Matrix Factorization
Time Series Forecasting
Temporal Regularized Matrix Factorization
Graph Regularization
Zeng, Jianfeng
Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets
author Zeng, Jianfeng
author_facet Zeng, Jianfeng
author_sort Zeng, Jianfeng
title Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets
title_short Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets
title_full Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets
title_fullStr Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets
title_full_unstemmed Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets
title_sort time series forecasting using temporal regularized matrix factorization and its application to traffic speed datasets
publisher University of Cincinnati / OhioLINK
publishDate 2021
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617109307510099
work_keys_str_mv AT zengjianfeng timeseriesforecastingusingtemporalregularizedmatrixfactorizationanditsapplicationtotrafficspeeddatasets
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