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
Short-term trend prediction in financial time series data
Date
2019-10-01
Author
Ozorhan, Mustafa Onur
Toroslu, İsmail Hakkı
Şehitoğlu, Onur Tolga
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
307
views
0
downloads
Cite This
This paper presents a method to predict short-term trends in financial time series data found in the foreign exchange market. Trends in the Forex market appear with similar chart patterns. We approach the chart patterns in the financial markets from a discovery of motifs in a time series perspective. Our method uses a modified Zigzag technical indicator to segment the data and discover motifs, expectation maximization to cluster the motifs and support vector machines to classify the motifs and predict accurate trading parameters for the identified motifs. The available input data are adapted to each trading time frame with a sliding window. The accuracy of the prediction models is tested across several different currency pairs, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using the Zigzag technical indicator to discover motifs that identify short-term trends in financial data results in a high prediction accuracy and trade profits.
Subject Keywords
Human-Computer Interaction
,
Hardware and Architecture
,
Software
,
Artificial Intelligence
,
Information Systems
URI
https://hdl.handle.net/11511/42572
Journal
KNOWLEDGE AND INFORMATION SYSTEMS
DOI
https://doi.org/10.1007/s10115-018-1303-x
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Semantic information-based alternative plan generation for multiple query optimization
Polat, Faruk; Alhajj, R (Elsevier BV, 2001-09-01)
This paper addresses the impact of semantic information about queries on alternative plan generation (APG) for multiple query optimization (MQO). MQO covers optimizing the execution of a set of queries together where each query in the set to be optimized has several alternative execution plans. A multiple query optimizer selects an alternative plan for each query to obtain an optimal global execution plan. Our approach uses information such as common relations, common possible joins and common conditions to...
Graph-based multilevel temporal video segmentation
Sakarya, Ufuk; TELATAR, ZİYA (Springer Science and Business Media LLC, 2008-11-01)
This paper presents a graph-based multilevel temporal video segmentation method. In each level of the segmentation, a weighted undirected graph structure is implemented. The graph is partitioned into clusters which represent the segments of a video. Three low-level features are used in the calculation of temporal segments' similarities: visual content, motion content and shot duration. Our strength factor approach contributes to the results by improving the efficiency of the proposed method. Experiments sho...
Forecasting direction of exchange rate fluctuations with two dimensional patterns and currency strength
Özorhan, Mustafa Onur; Toroslu, İsmail Hakkı; Department of Computer Engineering (2017)
This thesis presents a method to predict the direction and magnitude of movement of currency pairs in the foreign exchange market. The method uses clustering and classification methods with a combination of two dimensional chart patterns, processed price data and technical indicator data. The input data is adapted to each trading day with a moving time-frame. The accuracy of the prediction models are tested across several different currency pairs. The experimental results suggest that using two dimensional ...
Time-Varying Linkage between Equities and Oil
Ordu Akkaya, Beyza Mina; Oran, Adil; Soytaş, Uğur (World Scientific Publishing, 2020-01-01)
This study examines the correlation structures between oil futures, the S&P 500 and US sectoral indices, using the Asymmetric DCC method. The results indicate that these correlations display time-varying and asymmetric characteristics. The potential effects of variousfactors, including copper and gold prices, dollar/euro exchange rate, T-bill rate and financial stress index on these dynamic correlations are also investigated. The dynamic links betweenoil and stock returns weaken in response to shocks in all...
Improving reinforcement learning by using sequence trees
Girgin, Sertan; Polat, Faruk; Alhajj, Reda (Springer Science and Business Media LLC, 2010-12-01)
This paper proposes a novel approach to discover options in the form of stochastic conditionally terminating sequences; it shows how such sequences can be integrated into the reinforcement learning framework to improve the learning performance. The method utilizes stored histories of possible optimal policies and constructs a specialized tree structure during the learning process. The constructed tree facilitates the process of identifying frequently used action sequences together with states that are visit...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. O. Ozorhan, İ. H. Toroslu, and O. T. Şehitoğlu, “Short-term trend prediction in financial time series data,”
KNOWLEDGE AND INFORMATION SYSTEMS
, pp. 397–429, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42572.