Abstract
The prediction of time-series data is a challenging and complex issue. For many practical applications, network topology information and text information can play a perfect role in time-series prediction. This article takes stock data as an example by constructing a graph, connecting each stock’s upstream and downstream industries, and obtaining useful text features and topological features to predict the stock time-series. Based on the time-series data features, text features, and the topological features of the stock industry chain of machine learning, we compared our prediction model with other fuzzy time-series prediction methods, which are only based on historical features. The experiment shows that our method is better than the other methods in terms of the performance of multiple stocks in each stock’s time-series prediction. The experimental results show that the stock topology based on the industrial chain effectively improved time-series forecasting accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdollahzade M, Miranian A, Hassani H, Iranmanesh H (2015) A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting. Inf Sci 295:107–125
Efendi R, Ismail Z, Deris MM (2015) A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand. Appl Soft Comput 28:422–430
Miranian A, Rouzbehi K (2013) Nonlinear power system load identification using local model networks. IEEE Trans Power Syst 28:2872–2881
Sadaei HJ, Enayatifar R, Guimarães FG, Mahmud M, Alzamil ZA (2016) Combining ARFIMA models and fuzzy time series for the forecast of long memory time series. Neurocomputing 175:782–796
Sadaei HJ, Enayatifar R, Lee MH, Mahmud M (2016) A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting. Appl Soft Comput 40:132–149
Majhi B, Anish CM (2015) Multiobjective optimization based adaptive models with fuzzy decision making for stock market forecasting. Neurocomputing 167:502–511
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8:199–249
Garg B, Beg MMS, Ansari AQ (2012) A new computational fuzzy time series model to forecast number of outpatient visits. In IEEE annual meeting of North American of fuzzy information processing society (NAFIPS). IEEE, Berkeley, pp 1–6
Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54:269–277
Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series-part II. Fuzzy Sets Syst 62:1–8
Duru O (2010) A fuzzy integrated logical forecasting model for dry bulk shipping index forecasting: an improved fuzzy time series approach. Expert Syst Appl 37:5372–5380
Domańska D, Wojtylak M (2012) Application of fuzzy time series models for forecasting pollution concentrations. Expert Syst Appl 39:7673–7679
Huarng K, Yu TH, Hsu YW (2007) A multivariate heuristic model for fuzzy time-series forecasting. IEEE Trans Syst Man Cybern Part B (Cybern) 37:836–846
Sun B, Guo H, Karimi HR, Ge Y, Xiong S (2015) Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing 151:1528–1536
Su C-H, Cheng C-H (2016) A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing 205:264–273
Talarposhti FM, Sadaei HJ, Enayatifar R, Guimarães FG, Mahmud M, Eslami T (2016) Stock market forecasting by using a hybrid model of exponential fuzzy time series. Int J Approx Reason 70:79–98
Maddalena Lucia, Petrosino Alfredo (2010) A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Comput Appl 19(2):179–186
Kerui X, Haibo G, Liang D, Guangjun L, Zongquan D, Zhen L, Changyou M (2016) Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended kalman filtering. Neural Comput Appl
Abdelwahab M, Parque V, Fath Elbab AMR, Abouelsoud AA, Sugano S (2020) Trajectory tracking of wheeled mobile robots using z-number based fuzzy logic. IEEE Access 8:18426–18441
Antonio M, Masao N (2020) Autonomous driving of truck-trailer mobile robots with linear-fuzzy control for trajectory following. In: 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE)
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Chen S-M (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81:311–319
Chen S-M, Chen C-D (2011) Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst Appl 38:3857–3864
Singh P (2017) A brief review of modeling approaches based on fuzzy time series. Int J Mach Learn Cybernet 8:397–420
Huarng K (2001) Heuristic models of fuzzy time series for forecasting. Fuzzy Sets Syst 123:369–386
Hsu Y-Y, Tse S-M, Wu B (2003) A new approach of bivariate fuzzy time series analysis to the forecasting of a stock index. Int J Uncertain Fuzziness Knowl Based Syst 11:671–690
Chen T-L, Cheng C-H, Teoh H-J (2008) High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets. Phys A Stat Mech Appl 387:876–888
Huarng K, Yu H-K (2005) A Type 2 fuzzy time series model for stock index forecasting. Phys A Stat Mech Appl 353:445–462
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, Z., Li, Y. & Liu, H. Fuzzy time-series prediction model based on text features and network features. Neural Comput & Applic 35, 3639–3649 (2023). https://doi.org/10.1007/s00521-021-05834-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-05834-w