A Financial Embedded Vector Model and Its Applications to Time Series Forecasting

Yanfeng Sun, Minglei Zhang, Si Chen, Xiaohu Shi


Inspired by the embedding representation in Natural Language Processing (NLP), we develop a financial embedded vector representation model to abstract the temporal characteristics of financial time series. Original financial features are discretized firstly, and then each set of discretized features is considered as a “word” of NLP, while the whole financial time series corresponds to the “sentence” or “paragraph”. Therefore the embedded vector models in NLP could be applied to the financial time series. To test the proposed model, we use RBF neural networks as regression model to predict financial series by comparing the financial embedding vectors as input with the original features. Numerical results show that the prediction accuracy of the test data is improved for about 4-6 orders of magnitude, meaning that the financial embedded vector has a strong generalization ability.


Embedded Vector; Financial Daily Vector; Financial Weekly Vector; RBF Neural Network

Full Text:



Scheffer M, Carpenter S R, Lenton T M, Bascompte J, Brock W, Dakos V, Van De Koppel J, Van De Leemput I A, Levin S A, Van Nes E H, Pascual M, Vandermeer J. Anticipating Critical Transitions [J]. Science, 2012, 338(6105): 344-348.

Cavalcante R C, Brasileiro R C, Souza V L F, Nobrega J P, Oliveira A L I. Computational Intelligence and Financial Markets: A Survey and Future Directions [J]. Expert Systems with Applications, 2016, 55: 194-211.

Tsai C F, Hsiao Y C. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches [J]. Decision Support Systems, 2010, 50(1): 258-269.

Jasemi M, Kimiagari A M, Memariani A. A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick [J]. Expert Systems with Applications, 2011, 38(4): 3884-3890.

Shen W, Guo X, Wu C, Wu D. Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm [J]. Knowledge-Based Systems, 2011, 24(3): 378-385.

Xiong T, Bao Y, Hu Z, Chiong R. Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms [J]. Information Sciences, 2015, 305: 77-92.

Akbilgic O, Bozdogan H, Balaban M E. A novel Hybrid RBF Neural Networks model as a forecaster [J]. Statistics and Computing, 2014, 24(3): 365-375.

Cao L. Support vector machines experts for time series forecasting [J]. Neurocomputing, 2003, 51: 321-339.

Wang J, Hou R, Wang C, Shen L. Improved v-Support vector regression model based on variable selection and brain storm optimization for stock price forecasting [J]. Applied Soft Computing, 2016, 49: 164-178.

Shen F, Chao J, Zhao J. Forecasting exchange rate using deep belief networks and conjugate gradient method [J]. Neurocomputing, 2015, 167: 243-253.

Kuremoto T, Kimura S, Kobayashi K, Obayashi M. Time series forecasting using a deep belief network with restricted Boltzmann machines [J]. Neurocomputing, 2014, 137: 47-56.

Pulido M, Melin P, Castillo O. Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange [J]. Information Sciences, 2014, 280: 188-204.

Lecun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.

Al-Ayyoub M, Nuseir A, Alsmearat K, Jararweh Y, Gupta B. Deep learning for Arabic NLP: A survey [J]. Journal of Computational Science.

Khan W, Daud A, Nasir A A, Amjad T. A survey on the state-of-the-art machine learning models in the context of NLP [J]. Kuwait Journal of Science, 2016, 43(4): 95-113.

Sun S, Luo C, Chen J. A review of natural language processing techniques for opinion mining systems [J]. Information Fusion, 2017, 36(Supplement C): 10-25.

Turian J, Ratinov L, Bengio Y. Word representations: a simple and general method for semi-supervised learning[A]. In proceedings of the 48th Annual Meeting of the Association for Computational Linguistics[C]. Uppsala, Sweden: Association for Computational Linguistics, 2010:384-394

Deerwester S, Dumais S T, Furnas G W, Landauer T K, Harshman R. Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 1990, 41: 391-407.

Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation. Journal of Machine Learning Research [J]. 2003, 3 (4-5): 993-1022.

Bengio Y, Ducharme J, Vincent P, Janvin C. A neural probabilistic language model [J]. The Journal of Machine Learning Research, 2003, 3(2): 1137-1155.

Mikolov T, Chen K, Corrado G, Dean J. Efficient Estimation of Word Representations in Vector Space[J]. In proceedings of 1st International Conference on Learning Representations (ICLR2013). Scottsdale, AZ, USA, 2013.

Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed Representations of Words and Phrases and their Compositionality[A]. In proceedings of the 26th International Conference on Neural Information Processing Systems[C]. Lake Tahoe, Nevada: Curran Associates Inc., 2013:3111-3119.

Le Q V, Mikolov T. Distributed Representations of Sentences and Documents[A]. In proceedings of the 31st International Conference on Machine Learning[C]. Beijing, China, 2014:1188-1196.

Singh S P, Kumar A, Darbari H, Singh L, Rastogi A, Jain S. Machine translation using deep learning: An overview[A]. In proceedings of 2017 International Conference on Computer, Communications and Electronics (Comptelix)[C], 2017:162-167.

Frome A, Corrado G, Shlens J, Bengio S, Dean J, Ranzato M A, Mikolov T. DeViSE: A Deep Visual-Semantic Embedding Model[A]. In proceedings of Advances in Neural Information Processing Systems 26 (NIPS 2013)[C]. Lake Tahoe, Nevada, USA: Curran Associates, Inc., 2013:2121-2129.

Moyano L G. Learning network representations [J]. The European Physical Journal Special Topics, 2017, 226(3): 499-518.

Perozzi B, Al-Rfou R, Skiena S. DeepWalk: online learning of social representations[A]. In proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining[C]. New York, USA: ACM, 2014:701-710.

Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q. LINE: Large-scale Information Network Embedding[A]. In proceedings of the 24th International Conference on World Wide Web[C]. Florence, Italy: International World Wide Web Conferences Steering Committee, 2015:1067-1077.

Yahoo finance [DB/OL]. http://finance.yahoo.com, 2017, 1,1.

DOI: https://doi.org/10.15837/ijccc.2018.5.3286

Copyright (c) 2018 Yanfeng Sun, Minglei Zhang, Si Chen, Xiaohu Shi

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

CC-BY-NC  License for Website User

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]

INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2018: IF=1.585..

IJCCC is indexed in Scopus from 2008 (CiteScore2018 = 1.56):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.


 Impact Factor in JCR2018 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.585 (Q3). Scopus: CiteScore2018=1.56 (Q2);

SCImago Journal & Country Rank

Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.