An Improved Quantile-Point-Based Evolutionary Segmentation Representation Method of Financial Time Series Article Swipe
Lei Liu
,
Zheng Pei
,
Peng Chen
,
Zhisheng Gao
,
Zhihao Gan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.34028/iajit/19/6/4
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.34028/iajit/19/6/4
Effective and concise feature representation is crucial for time series mining. However, traditional time series feature representation approaches are inadequate for Financial Time Series (FTS) due to FTS' complex, highly noisy, dynamic and non-linear characteristics. Thus, we proposed an improved linear segmentation method named MS-BU-GA in this work. The critical data points that can represent financial time series are added to the feature representation result. Specifically, firstly, we propose a division criterion based on the quantile segmentation points. On the basis of this criterion, we perform segmentation of the time series under the constraint of the maximum segment fitting error. Then, a bottom-up mechanism is adopted to merge the above segmentation results under the maximum segment fitting error. Next, we apply Genetic Algorithm (GA) to the merged results for further optimization, which reduced the overall segment representation fitting error and the integrated factor of segment representation error and number of segments. The experimental result shows that the MS-BU-GA has outperformed existing methods in segment number and representation error. The overall average representation error is decreased by 21.73% and the integrated factor of the number of segments and the segment representation error is reduced by 23.14%.
Related Topics
Concepts
Segmentation
Computer science
Representation (politics)
Quantile
Series (stratigraphy)
Pattern recognition (psychology)
Algorithm
Merge (version control)
Time series
Feature (linguistics)
Constraint (computer-aided design)
Artificial intelligence
Mathematics
Statistics
Machine learning
Philosophy
Paleontology
Geometry
Law
Linguistics
Political science
Information retrieval
Politics
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.34028/iajit/19/6/4
- OA Status
- diamond
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312900619
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312900619Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.34028/iajit/19/6/4Digital Object Identifier
- Title
-
An Improved Quantile-Point-Based Evolutionary Segmentation Representation Method of Financial Time SeriesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Lei Liu, Zheng Pei, Peng Chen, Zhisheng Gao, Zhihao GanList of authors in order
- Landing page
-
https://doi.org/10.34028/iajit/19/6/4Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.34028/iajit/19/6/4Direct OA link when available
- Concepts
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Segmentation, Computer science, Representation (politics), Quantile, Series (stratigraphy), Pattern recognition (psychology), Algorithm, Merge (version control), Time series, Feature (linguistics), Constraint (computer-aided design), Artificial intelligence, Mathematics, Statistics, Machine learning, Philosophy, Paleontology, Geometry, Law, Linguistics, Political science, Information retrieval, Politics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Series | 24 |
| abstract_inverted_index.error. | 100, 118, 168 |
| abstract_inverted_index.factor | 143, 181 |
| abstract_inverted_index.highly | 30 |
| abstract_inverted_index.linear | 41 |
| abstract_inverted_index.merged | 127 |
| abstract_inverted_index.method | 43 |
| abstract_inverted_index.noisy, | 31 |
| abstract_inverted_index.number | 149, 165, 184 |
| abstract_inverted_index.points | 52 |
| abstract_inverted_index.result | 154 |
| abstract_inverted_index.series | 10, 15, 58, 91 |
| abstract_inverted_index.23.14%. | 195 |
| abstract_inverted_index.Genetic | 122 |
| abstract_inverted_index.adopted | 106 |
| abstract_inverted_index.average | 171 |
| abstract_inverted_index.concise | 3 |
| abstract_inverted_index.crucial | 7 |
| abstract_inverted_index.dynamic | 32 |
| abstract_inverted_index.feature | 4, 16, 63 |
| abstract_inverted_index.fitting | 99, 117, 138 |
| abstract_inverted_index.further | 130 |
| abstract_inverted_index.maximum | 97, 115 |
| abstract_inverted_index.methods | 162 |
| abstract_inverted_index.mining. | 11 |
| abstract_inverted_index.overall | 135, 170 |
| abstract_inverted_index.perform | 86 |
| abstract_inverted_index.points. | 78 |
| abstract_inverted_index.propose | 69 |
| abstract_inverted_index.reduced | 133, 193 |
| abstract_inverted_index.result. | 65 |
| abstract_inverted_index.results | 112, 128 |
| abstract_inverted_index.segment | 98, 116, 136, 145, 164, 189 |
| abstract_inverted_index.However, | 12 |
| abstract_inverted_index.MS-BU-GA | 45, 158 |
| abstract_inverted_index.complex, | 29 |
| abstract_inverted_index.critical | 50 |
| abstract_inverted_index.division | 71 |
| abstract_inverted_index.existing | 161 |
| abstract_inverted_index.firstly, | 67 |
| abstract_inverted_index.improved | 40 |
| abstract_inverted_index.proposed | 38 |
| abstract_inverted_index.quantile | 76 |
| abstract_inverted_index.segments | 186 |
| abstract_inverted_index.Abstract: | 0 |
| abstract_inverted_index.Algorithm | 123 |
| abstract_inverted_index.Effective | 1 |
| abstract_inverted_index.Financial | 22 |
| abstract_inverted_index.bottom-up | 103 |
| abstract_inverted_index.criterion | 72 |
| abstract_inverted_index.decreased | 175 |
| abstract_inverted_index.financial | 56 |
| abstract_inverted_index.mechanism | 104 |
| abstract_inverted_index.represent | 55 |
| abstract_inverted_index.segments. | 151 |
| abstract_inverted_index.approaches | 18 |
| abstract_inverted_index.constraint | 94 |
| abstract_inverted_index.criterion, | 84 |
| abstract_inverted_index.inadequate | 20 |
| abstract_inverted_index.integrated | 142, 180 |
| abstract_inverted_index.non-linear | 34 |
| abstract_inverted_index.traditional | 13 |
| abstract_inverted_index.experimental | 153 |
| abstract_inverted_index.outperformed | 160 |
| abstract_inverted_index.segmentation | 42, 77, 87, 111 |
| abstract_inverted_index.Specifically, | 66 |
| abstract_inverted_index.optimization, | 131 |
| abstract_inverted_index.representation | 5, 17, 64, 137, 146, 167, 172, 190 |
| abstract_inverted_index.characteristics. | 35 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.56616245 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |