Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification
In this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. Existing financial forecasting models often struggle with the inherent volatility and compl...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11058928/ |
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| author | Seyed Ali Hosseini Francesco Grimaccia Alessandro Niccolai Silvia Trimarchi |
| author_facet | Seyed Ali Hosseini Francesco Grimaccia Alessandro Niccolai Silvia Trimarchi |
| author_sort | Seyed Ali Hosseini |
| collection | DOAJ |
| description | In this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. Existing financial forecasting models often struggle with the inherent volatility and complexity of financial markets, particularly due to their reliance on traditional financial data features which fail to capture intricate price patterns. This research addresses the critical gap in effectively identifying and leveraging these patterns to improve predictive accuracy. By collecting tick-by-tick data from the European Carbon Emission Allowance market and resampling it to a 15-minute timeframe, we developed a method to detect pivotal price patterns and extract relevant features. Integrating these features with traditional open, high, low, close, and volume (OHLCV) data in long-short-term memory (LSTM) and gated recurrent unit (GRU) combined with dense neural networks, our empirical results demonstrate significant improvements in model performance, showcasing enhanced accuracy compared to models using only traditional data features. |
| format | Article |
| id | doaj-art-264d10b752ae4d5ebffa38eb57f9977e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-264d10b752ae4d5ebffa38eb57f9977e2025-08-20T03:31:37ZengIEEEIEEE Access2169-35362025-01-011311334311335510.1109/ACCESS.2025.358425111058928Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series ClassificationSeyed Ali Hosseini0https://orcid.org/0009-0005-5296-4009Francesco Grimaccia1https://orcid.org/0000-0003-2568-9927Alessandro Niccolai2https://orcid.org/0000-0002-5840-4222Silvia Trimarchi3https://orcid.org/0000-0001-5741-7833Department of Energy, Polytechnic University of Milan, Milan, ItalyDepartment of Energy, Polytechnic University of Milan, Milan, ItalyDepartment of Energy, Polytechnic University of Milan, Milan, ItalyDepartment of Energy, Polytechnic University of Milan, Milan, ItalyIn this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. Existing financial forecasting models often struggle with the inherent volatility and complexity of financial markets, particularly due to their reliance on traditional financial data features which fail to capture intricate price patterns. This research addresses the critical gap in effectively identifying and leveraging these patterns to improve predictive accuracy. By collecting tick-by-tick data from the European Carbon Emission Allowance market and resampling it to a 15-minute timeframe, we developed a method to detect pivotal price patterns and extract relevant features. Integrating these features with traditional open, high, low, close, and volume (OHLCV) data in long-short-term memory (LSTM) and gated recurrent unit (GRU) combined with dense neural networks, our empirical results demonstrate significant improvements in model performance, showcasing enhanced accuracy compared to models using only traditional data features.https://ieeexplore.ieee.org/document/11058928/Energy marketfinancial pattern extractionfinancial feature extractiondeep learningfinancial data classification |
| spellingShingle | Seyed Ali Hosseini Francesco Grimaccia Alessandro Niccolai Silvia Trimarchi Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification IEEE Access Energy market financial pattern extraction financial feature extraction deep learning financial data classification |
| title | Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification |
| title_full | Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification |
| title_fullStr | Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification |
| title_full_unstemmed | Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification |
| title_short | Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification |
| title_sort | pattern based feature extraction for improved deep learning in financial time series classification |
| topic | Energy market financial pattern extraction financial feature extraction deep learning financial data classification |
| url | https://ieeexplore.ieee.org/document/11058928/ |
| work_keys_str_mv | AT seyedalihosseini patternbasedfeatureextractionforimproveddeeplearninginfinancialtimeseriesclassification AT francescogrimaccia patternbasedfeatureextractionforimproveddeeplearninginfinancialtimeseriesclassification AT alessandroniccolai patternbasedfeatureextractionforimproveddeeplearninginfinancialtimeseriesclassification AT silviatrimarchi patternbasedfeatureextractionforimproveddeeplearninginfinancialtimeseriesclassification |