Abstract
The prices of the stock are influence by many factors and emerge extremely nonlinear structure. Therefore, the stock trading prediction and recommendation is an extremely challenging task. In this paper, a novel stock trading prediction and recommendation system is proposed in user-friendly form. The recommendation system can inform the user whether is to buy or sell the stocks in the next step. Information granulation is applied to transform raw time series into meaningful and interpretable granules, and the more effective non-uniform partitioning method for prediction is presented. The system first determines the intervals based on information granules, and then define the fuzzy sets and fuzzify the historical data. Third, construct fuzzy relationships and assign weights to each period. Finally, the prediction and recommendation is implemented. The experimental results show the proposed system yields better prediction performance, and increases profit-making opportunities.
Similar content being viewed by others
References
Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81(3):311–319
Chen SM, Chen CD (2011) TAIEX Forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19(1):1–12
Fu IC, Chung FL, Ng CM (2006) Financial time series segmentation based on specialized binary tree representation Proceedings of the 2006 international conference on data mining, pp 3–9
Huarng K (2001) Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst 123(3):387–394
Huarng K, Yu THK (2006) The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications 363(2):481–491
Huarng K, Yu THK (2006) Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans Syst Man Cybern B Cybern 36(2):328–340
Huarng THK, Yu KH, Hsu YW (2007) A multivariate heuristic model for fuzzy time-series forecasting. IEEE Trans Syst Man Cybern B Cybern 37(4):836–846
Jiang J, Zhang Z, Wang H (2007) A New segmentation algorithm to stock time series based on PIP approach Proceedings of the international conference on wireless communications, networking and mobile computing, pp 5609–5612
Pedrycz W, Computing Granular (2013) Analysis and design of intelligent systems. CRC Press/Francis Taylor, Boca Raton
Pedrycz W, Homenda W (2013) Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 13:4209–4218
Pedrycz W, Succi G, Sillitti A, Iljazi J (2015) Data description: a general framework of information granules. Knowl-Based Syst 80:98–108
Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series–Part I. Fuzzy Sets Syst 54(1):1–9
Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54(3):269–277
Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series–Part II. Fuzzy Sets Syst 64(1):1–8
Sullivan J, Woodall WH (1994) A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets Syst 64(3):279–293
Wang WN, Liu XD (2015) Time series forecasting based on fuzzy data mining. ICIC Express Letters 9(9):2483–2489
Wang X, Pedrycz W, Gacek A, Liu X (2016) From numeric data to information granules: a design through clustering and the principle of justifiable granularity. Knowl-Based Syst 101:100–113
Wang ZJ, Willett P (2004) Joint segmentation and classification of time series using class-specific features. IEEE Trans Syst Man Cybern B Cybern 34:1056–1067
Wen JM, Chang XW (2017) Success probability of the Babai estimators for box-constrained integer linear models. IEEE Trans Inf Theory 63:631–648
Wen JM, Li DF, Zhu FM (2015) Stable recovery of sparse signals via L p -minimization. Appl Comput Harmon Anal 38:161–176
Wen JM, Zhou ZC, Wang J, Tang XH, Mo Q (2017) A sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit. IEEE Trans Signal Process 65:1370–1382
Yu HK (2004) Weighted fuzzy time-series models for TAIEX forecasting. Physica A: Statistical Mechanics and its Applications 349(3/4):609–624
Yu HK (2005) A refined fuzzy time-series model for forecasting. Physica A: Statistical Mechanics and its Applications 346(3/4):657–681
Yu THK, Huarng KH (2008) A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst Appl 34(4):2945–2952
Yu THK, Huarng KH (2010) Corrigendum to “A bivariate fuzzy time series model to forecast the TAIEX”. Expert Syst Appl 37(7):5529
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, W., Mishra, K.K. A novel stock trading prediction and recommendation system. Multimed Tools Appl 77, 4203–4215 (2018). https://doi.org/10.1007/s11042-017-4587-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-4587-z