1D convolutional neural networks for chart pattern classification in financial time series
This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural ne...
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| Vydáno v: | The Journal of supercomputing Ročník 78; číslo 12; s. 14191 - 14214 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.08.2022
Springer Nature B.V |
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural networks (2D CNNs). In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping. Experimental results on synthetic datasets reveal that the accuracy of 1D CNN is highest among all the methods evaluated. Results on real datasets also reveal that chart patterns identified by 1D CNN are also the most recognized instances when they are compared to those classified by other methods. |
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| AbstractList | This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural networks (2D CNNs). In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping. Experimental results on synthetic datasets reveal that the accuracy of 1D CNN is highest among all the methods evaluated. Results on real datasets also reveal that chart patterns identified by 1D CNN are also the most recognized instances when they are compared to those classified by other methods. |
| Author | Si, Yain-Whar Liu, Liying |
| Author_xml | – sequence: 1 givenname: Liying surname: Liu fullname: Liu, Liying organization: Department of Computer and Information Science, University of Macau – sequence: 2 givenname: Yain-Whar orcidid: 0000-0001-8468-6182 surname: Si fullname: Si, Yain-Whar email: fstasp@umac.mo organization: Department of Computer and Information Science, University of Macau |
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| Cites_doi | 10.1016/j.eswa.2016.02.017 10.1007/978-3-642-24797-2 10.1145/2339530.2339576 10.1016/j.asoc.2019.105700 10.1016/j.asoc.2017.03.023 10.1016/j.asoc.2021.107460 10.1186/s40854-020-00187-0 10.1145/130385.130401 10.1145/3468891.3468896 10.1145/3383455.3422544 10.1109/TNSRE.2017.2721116 10.1016/j.ins.2017.05.028 10.1007/978-0-387-30162-4_415 10.1007/s10618-019-00619-1 10.33793/acperpro.03.01.89 10.1007/s00500-017-2703-7 10.3390/pr9091484 10.1016/j.engappai.2006.07.003 10.1016/j.neunet.2005.06.042 10.1109/CVPR.2015.7298594 10.1016/j.patcog.2017.10.013 10.1109/ICASSP.2019.8682194 |
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| Keywords | Chart patterns 1D convolutional neural networks Financial time series Classification |
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ACM WanYSiYWAdaptive neuro fuzzy inference system for chart pattern matching in financial time seriesAppl Soft Comput20175711810.1016/j.asoc.2017.03.023 Oord AVD, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. arXiv preprintarXiv:1609.03499 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 HuangGBZhuQYSiewCKExtreme learning machine: a new learning scheme of feedforward neural networksNeural Netw20042985990 LeCun Y et al. (2015) Lenet-5, convolutional neural networks. URL: http://yann.lecun.com/exdb/lenet, 20:5 Murphy JJ (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Penguin Cortes C, Vapnik V (June 17 1997). Soft margin classifier. 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Encyclopedia of Algorithms, pp 928–932 LinYLiuSYangHWuHJiangBImproving stock trading decisions based on pattern recognition using machine learning technologyPLOS ONE2021168125 GuJiuxiangWangZhenhuaKuenJasonMaLianyangShahroudyAmirShuaiBingLiuTingWangXingxingWangGangCaiJianfeiRecent advances in convolutional neural networksPattern Recognit20187735437710.1016/j.patcog.2017.10.013 WanYYwSiA hidden semi-markov model for chart pattern matching in financial time seriesSoft Comput201822196525654410.1007/s00500-017-2703-7 www.tradingview.com. Tradingview. 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Association for Computing Machinery Cheng C-S, Ho Y, Chiu T-C (2021) End-to-end control chart pattern classification using a 1d convolutional neural network and transfer learning. Processes, 9(9) Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp 144–152. ACM Chen J-H Tsai Y-C 2020) Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation 6 Y Wan (4431_CR36) 2017; 411 Jiuxiang Gu (4431_CR15) 2018; 77 4431_CR23 GB Huang (4431_CR19) 2004; 2 4431_CR22 4431_CR24 4431_CR26 A Supratak (4431_CR30) 2017; 25 4431_CR29 4431_CR28 Tak-chung Fu (4431_CR10) 2007; 20 4431_CR40 W Hu (4431_CR18) 2019; 84 4431_CR21 4431_CR20 Xueyuan Gong (4431_CR14) 2016; 55 4431_CR7 4431_CR6 4431_CR5 4431_CR4 Alex Graves (4431_CR13) 2005; 18 Y Wan (4431_CR35) 2017; 57 4431_CR3 4431_CR2 4431_CR1 4431_CR12 Y Lin (4431_CR25) 2021; 16 4431_CR34 4431_CR33 4431_CR16 4431_CR38 4431_CR9 4431_CR8 4431_CR17 4431_CR39 AH Moghaddam (4431_CR27) 2021; 108 Y Wan (4431_CR37) 2018; 22 HI Fawaz (4431_CR11) 2019; 33 4431_CR32 4431_CR31 |
| References_xml | – reference: WanYSiYWA formal approach to chart patterns classification in financial time seriesInf Sci201741115117510.1016/j.ins.2017.05.028 – reference: HuangGBZhuQYSiewCKExtreme learning machine: a new learning scheme of feedforward neural networksNeural Netw20042985990 – reference: Xu C (2021) Image-based candlestick pattern classification with machine learning. In: 2021 6th International Conference on Machine Learning Technologies, ICMLT 2021, pp 26-33, New York, NY, USA. Association for Computing Machinery – reference: LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw 3361(10):1995 – reference: www.tradingview.com. Tradingview. Accessed January 23, 2021 – reference: FuTak-chungChungFu-laiLukRobertNgChak-manStock time series pattern matching: template-based vs. rule-based approachesEng Appl Artif Intell200720334736410.1016/j.engappai.2006.07.003 – reference: Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 – reference: Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: KDD workshop, 10, 359–370. Seattle, WA – reference: Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2019) 1D convolutional neural networks and applications: a survey. arXiv preprint arXiv:1905.03554 – reference: Murphy JJ (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Penguin – reference: WanYYwSiA hidden semi-markov model for chart pattern matching in financial time seriesSoft Comput201822196525654410.1007/s00500-017-2703-7 – reference: Cortes C, Vapnik V (June 17 1997). Soft margin classifier. US Patent 5,640,492 – reference: Zhang Z, Jiang J, Liu X, Lau R, Wang H, Zhang R (2010) A real time hybrid pattern matching scheme for stock time series. In Proceedings of the Twenty-First Australasian Conference on Database Technologies-Volume 104, pp 161–170. Australian Computer Society, Inc – reference: Chen J-H Tsai Y-C 2020) Encoding candlesticks as images for pattern classification using convolutional neural networks. Financial Innovation 6 – reference: Kaya C, Yilmaz A, Uzun GN, Kilimci ZH (2020) Stock pattern classification from charts using deep learning algorithms. In: 8th International symposium on innovative technologies in engineering and science, pp 445–454 – reference: Hussain M, Haque MA et al. (2018) Swishnet: a fast convolutional neural network for speech, music and noise classification and segmentation. arXiv preprint arXiv:1812.00149 – reference: Cristianini Nello, Ricci Elisa (2008) Support vector machines: 1992; boser, guyon, vapnik. Encyclopedia of Algorithms, pp 928–932 – reference: GuJiuxiangWangZhenhuaKuenJasonMaLianyangShahroudyAmirShuaiBingLiuTingWangXingxingWangGangCaiJianfeiRecent advances in convolutional neural networksPattern Recognit20187735437710.1016/j.patcog.2017.10.013 – reference: Oord AVD, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. arXiv preprintarXiv:1609.03499 – reference: WanYSiYWAdaptive neuro fuzzy inference system for chart pattern matching in financial time seriesAppl Soft Comput20175711810.1016/j.asoc.2017.03.023 – reference: GongXueyuanSiYain-WharFongSimonBiuk-AghaiRobert PFinancial time series pattern matching with extended ucr suite and support vector machineExpert Syst Appl20165528429610.1016/j.eswa.2016.02.017 – reference: Hochreiter S, Schmidhuber J (1997) Lstm can solve hard long time lag problems. In: Advances in neural information processing systems, pp 473–479 – reference: MoghaddamAHMomtaziSImage processing meets time series analysis: predicting forex profitable technical pattern positionsAppl Soft Comput202110810746010.1016/j.asoc.2021.107460 – reference: HuWSiYWFongSLauRYKA formal approach to candlestick pattern classification in financial time seriesAppl Soft Comput20198410.1016/j.asoc.2019.105700 – reference: LinYLiuSYangHWuHJiangBImproving stock trading decisions based on pattern recognition using machine learning technologyPLOS ONE2021168125 – reference: Wang Z, Oates T (2015) Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence – reference: Bulkowski TN (2011) Encyclopedia of chart patterns, 2nd edn. John Wiley & Sons – reference: FawazHIForestierGWeberJIdoumgharLMullerPADeep learning for time series classification: a reviewData Min Knowl Discov2019334917963396203910.1007/s10618-019-00619-1 – reference: GravesAlexSchmidhuberJürgenFramewise phoneme classification with bidirectional lstm and other neural network architecturesNeural netw2005185–660261010.1016/j.neunet.2005.06.042 – reference: Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9 – reference: Graves A (2012) Supervised sequence labelling with recurrent neural networks. 2012. URL http://books.google.com/books – reference: Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. 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| Title | 1D convolutional neural networks for chart pattern classification in financial time series |
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