1. [1] Hung-Wen Peng, Shen-Fu Wu, Chia-Ching Wei, Shie-Jue Lee. (2015). Time series forecasting with a neuro-fuzzy modeling scheme. Elsevier Applied Soft Computing. Pages 481–493.
2. [2] Mrinalini Shah. (2012). Fuzzy based trend mapping and forecasting for time series data. Elsevier Expert Systems with Applications. Pages 6351–6358Knowledge-Based Systems. [
DOI:10.1016/j.eswa.2011.12.036]
3. [3] S. Askari , N. Montazerin. (2015). A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering. Elsevier. Expert Systems with Applications (2015) 2121–2135. [
DOI:10.1016/j.eswa.2014.09.036]
4. [4] Ramezani Mouziraji Farhad , Yaghoobi Mehdi , Ghanghermeh Abdolazim. (2011). Caspian Sea Level Prediction Based On Fuzzy Regressor System. Scientific Information Database (SID) Journal: WATER AND WASTEWATER; Page(s) 90 To 98.
5. [5] Gholamali Heydari, MohammadAli Vali , Ali Akbar Gharaveisi.(2016). Chaotic time series prediction via artificial neural square fuzzy inference Elsevier Expert Systems with Applications , Pages 461–468.
6. [6] Sapankevych, N.I. ; Sankar, Ravi. (2009). Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Society. 1556-603X. [
DOI:10.1109/MCI.2009.932254]
7. [7] Vasilii A. Gromov, Artem N. Shulga. (2012). Chaotic time series prediction with employment of ant colony optimization. Elsevier, Expert Systems with Applications 39. 8474–8478. [
DOI:10.1016/j.eswa.2012.01.171]
8. [8] Mu-Yen Chen. (2014). A high-order fuzzy time series forecasting model for internet stock trading. Elsevier Future Generation Computer Systems.
9. [9] Ozge Cagcag Yolcu, Ufuk Yolcu, Erol Egrioglu, C. Hakan Aladag. (2016). High order fuzzy time series forecasting method based on an intersection operation. Elsevier Applied Mathematical Modelling.
10. [10] P. Singh. (2016). Chapter 2 Fuzzy Time Series Modeling Approaches : AReview. Springer. Applications of Soft Computing.
11. [11] Erol Egrioglu, Eren Bas, Cagdas Hakan Aladag, Ufuk Yolcu. (2016). Probabilistic Fuzzy Time Series Method Based on Artificial Neural Network. American Journal of Intelligent Systems. P-ISSN: 2165-8978 , E-ISSN: 2165-8994.
12. [12] Tak-chung Fu. (2011). A review on time series data mining. Elsevier, Engineering Applications of Artificial Intelligence 24, 164-181. [
DOI:10.1016/j.engappai.2010.09.007]
13. [13] Ahmed Kattan, Shaheen Fatima, Muhammad Arif. (2015). Time-series event-based prediction: An unsupervised learning framework based on genetic programming.Elsevier. Information Sciences 301 99–123. [
DOI:10.1016/j.ins.2014.12.054]
14. [14] Brad S. chisson. (1994). Forcasting Enrollmet With Fuzzy Time Series pII.. Elsevier.. Science. 0165-114(93)E0211-A.
15. [15] Singh, S. R. (2009). A computational methd of forecasting based on high-order fuzzy time series. Elsevier international Journal of applied Expert Systems with Applications 36 , 10551-10559. [
DOI:10.1016/j.eswa.2009.02.061]
16. [16] Aghili Setare. Omranpour Hesam. Motameni Homayun. (2014). Application of a Fuzzy method for predicting based on high-order time series. IEEE, 978-1-4799-3351-8/14/$31.00. [
PMID] [
PMCID]
17. [17] Omolbanin Yazdanbakhsh. Scott Dick. (2017). Forecasting of Multivariate Time Series via Complex Fuzzy Logic. IEEE, 2168-2216.
18. [18] Ping Jiang, Qingli Dong. Peizhi Li, Lanlan Lian. (2017). A novel high-order weighted fuzzy time series model and itsapplication in nonlinear time series prediction. Elsevier Applied Soft Computing. [
DOI:10.1016/j.asoc.2017.01.043]