Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Download
index.pdf
Date
2019-10-07
Author
Buyuksahin, Umit Cavus
Ertekin Bolelli, Şeyda
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
259
views
70
downloads
Cite This
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist that use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network (ANN) hybrid method that work in a more general framework. Experimental results show that strategies for decomposing the original data and for combining linear and nonlinear models throughout the hybridization process are key factors in the forecasting performance of the methods. By using these findings, the proposed hybrid method is combined with Empirical Mode Decomposition (EMD) technique which generates more predictable components. We show that our hybrid method with EMD can be an effective way to improve forecasting accuracy obtained by traditional hybrid methods and also any of the individual methods that we used separately.
Subject Keywords
Cognitive Neuroscience
,
Artificial Intelligence
,
Computer Science Applications
URI
https://hdl.handle.net/11511/43038
Journal
NEUROCOMPUTING
DOI
https://doi.org/10.1016/j.neucom.2019.05.099
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
ILP-based concept discovery in multi-relational data mining
Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (Elsevier BV, 2009-11-01)
Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, an ILP-based concept discov...
ANFIS_unfolded_in_time for multivariate time series forecasting
Sisman-Yilmaz, Na; Alpaslan, Ferda Nur; Jain, L (Elsevier BV, 2004-10-01)
This paper proposes a temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules. It is a modification of ANFIS neuro-fuzzy model. The rule base of ANFIS_unfolded_in_time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, back-propagation learning algorithm is used. The system takes the multivariate data and the number o...
Robust semi-supervised clustering with polyhedral and circular uncertainty
DİNLER, DERYA; Tural, Mustafa Kemal (Elsevier BV, 2017-11-22)
We consider a semi-supervised clustering problem where the locations of the data objects are subject to uncertainty. Each uncertainty set is assumed to be either a closed convex bounded polyhedron or a closed disk. The final clustering is expected to be in accordance with a given number of instance level constraints. The objective function considered minimizes the total of the sum of the violation costs of the unsatisfied instance level constraints and a weighted sum of squared maximum Euclidean distances b...
Generation of cyclic/toroidal chaos by Hopfield neural networks
Akhmet, Marat (Elsevier BV, 2014-12-05)
We discuss the appearance of cyclic and toroidal chaos in Hopfield neural networks. The theoretical results may strongly relate to investigations of brain activities performed by neurobiologists. As new phenomena, extension of chaos by entrainment of several limit cycles as well as the attraction of cyclic chaos by an equilibrium are discussed. Appropriate simulations that support the theoretical results are depicted. Stabilization of tori in a chaotic attractor is realized not only for neural networks, but...
Attraction of Li-Yorke chaos by retarded SICNNs
Akhmet, Marat (Elsevier BV, 2015-01-05)
In the present study, dynamics of retarded shunting inhibitory cellular neural networks (SICNNs) is investigated with Li-Yorke chaotic external inputs and outputs. Within the scope of our results, we prove the presence of generalized synchronization in coupled retarded SICNNs, and confirm it by means of the auxiliary system approach. We have obtained more than just synchronization, as it is proved that the Li-yorke chaos is extended with its ingredients, proximality and frequent separation, which have not b...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
U. C. Buyuksahin and Ş. Ertekin Bolelli, “Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition,”
NEUROCOMPUTING
, pp. 151–163, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43038.