Uncertain Fuzzy Time Series: Technical and Mathematical Review
Subject Areas : Journal of Computer & Robotics
1 - Department of Computer Engineering, Islamic Azad University, Shahr-e-Qods Branch, Iran, Tehran
Keywords:
Abstract :
[1] Song Q, Chissom B S, Forecasting enrollments with
fuzzy time series-part I, Fuzzy sets and systems,
Vol.54, No.1, 1-9, 1993.
[2] Song Q, Chissom B S, Fuzzy time series and its
models, Fuzzy sets and systems, Vol.54, No.3, 269-
277, 1993.
[3] Song Q, Chissom B S, Forecasting enrollments with
fuzzy time series-part II, Fuzzy sets and systems,
Vol.62, No.1, 1- 8, 1994.
[4] Chen S M, Forecasting enrollments based on fuzzy
time series, Fuzzy sets and systems, Vol.81, No.3,
311-319, 1996.
[5] Huarng K, Effective lengths of intervals to improve
forecasting in fuzzy time series, Fuzzy sets and
systems, Vol.123, No.3, 387-394, 2017.
[6] Kuo I H, Horng S J, Kao TW, et al, An improved
method for forecasting enrollments based on fuzzy
time series and particle swarm optimization, Expert
Systems with Applications, Vol.36, No.3, 6108-6117,
2017.
[7] Kuo I H, Horng S J, Chen Y H, et al, Forecasting
TAIFEX based on fuzzy time series and particle
swarm optimization, Expert Systems with
Applications, Vol.37, No.2, 1494-1502, 2020.
[8] Chen S M, Chung N Y, forecasting enrollments using
high order fuzzy time series and genetic algorithms,
International Journal of Intelligent Systems, Vol.21,
No.5, 485-501, 2016.
[9] Wang L, Liu X, PedryczW, Effective intervals
determined by information granules to improve
forecasting in fuzzy time series, Expert Systems with
Applications, Vol.40, No.14, 5673- 5679, 2013.
[10] LuW, Chen X, PedryczW, Liu X, Yang J. Using
interval information granules to improve forecasting in
fuzzy time series, International Journal of
Approximate Reasoning, Vol.57, 1-18, 2019.
[11] Wang L, Liu X, Pedrycz W, et al, Determination of
temporal information granules to improve forecasting
in fuzzy time series,Expert Systems with Applications,
Vol.41, No.6, 3134-3142, 2019.
[12] Huarng K, Yu H K, A type 2 fuzzy time series model
for stock index forecasting, Physica A: Statistical
Mechanics and its Applications, Vol.353, 445-462,
2015.
[13] Bajestani N S, Zare A, Application of optimized type
2 fuzzy time series to forecast Taiwan stock index, In
2009 2nd International Conference on Computer,
Control and Communication, 2018.
[14] Bajestani N S, Zare A, Forecasting TAIEX using
improved type 2 fuzzy time series, Expert Systems
with Applications, Vol.38, No.5, 5816-5821, 2011.
[15] Singh P, Borah B, Forecasting stock index price based
on Mfactors fuzzy time series and particle swarm
optimization, International Journal of Approximate
Reasoning, Vol.55, No.3, 2014.
[16] Aref Safari, Danial Barazandeh, Seyed Ali Khalegh
Pour. A Novel Fuzzy-C Means Image Segmentation
Model for MRI Brain Tumor Diagnosis. J. ADV
COMP ENG TECHNOL, 6(1) Winter 2020 : 19-2
[17] J. Kennedy, R. Eberhart and Y. Shi, Swarm
intelligence, ISBN: 1-55860-595-9, Morgan Kaufman,
2001. [48] Q. Song and B.S. Chissom, Fuzzy time
series and its models, Fuzzy Sets and Systems 54
[18] S.N. Sivanandam, S. Sumathi and S.N. Deepa,
Introduction to fuzzy logic using MATHLAB, ISBN:
103-540-35780-7, Springer-Verlag, 2007.
[19] A. Safari, R. Hosseini, M. Mazinani, A Novel Type-2
Adaptive Neuro Fuzzy Inference System Classifier for
Modelling Uncertainty in Prediction of Air Pollution
Disaster, IJE Transactions B: Applications, Vol 30,
No. 11, Pages 1746-1751, (2017).
[20] J.C. Dunn, A fuzzy relative of the ISODATA process
and its use in detecting compact well-separated
clusters, J. Cybernet. 3 (2019) 32–57.
[21] X. Golay, S. Kollias, G. Stoll, D. Meier, A. Valavanis,
P. Boesiger, A new correlation-based fuzzy logic
clustering algorithm for fMRI, Mag. Resonance Med.
40 (2018) 249–260.
[22] C.S. Muller-Levet, F. Klawonn, K.-H. Cho, O.
Wolkenhauer, Fuzzy clustering of short time series
and unevenly distributed sampling points, Proceedings
of the 5th International Symposium on Intelligent Data
Analysis, Berlin, Germany, August 28–30, 2018.
[23] M. Kumar, N.R. Patel, J. Woo, Clustering seasonality
patterns in the presence of errors, Proceedings of KDD
’02, Edmonton, Alberta, Canada.
[24] Y. Kakizawa, R.H. Shumway, N. Taniguchi,
Discrimination and clustering for multivariate time
series, J. Amer. Stat. Assoc. 93 (441) (2018) 328–340.
[25] R. Dahlhaus, On the Kullback–Leibler information
divergence of locally stationary processes, Stochastic
Process. Appl. 62 (2019) 139–168.
[26] R.H. Shumway, Time–frequency clustering and
discriminant analysis, Stat. Probab. Lett. 63 (2013)
307–314.
[27] J.C. Bezdek, N.R. Pal, Some new indexes of cluster
validity, IEEE Trans. Syst. Man Cybernet. B:
Cybernet. 28 (3) (2018) 301–315.
[28] C H López-Caraballo1, I Salfate1, J A Lazzús1, P
Rojas1, M Rivera1 and L Palma-Chilla1, Mackey-
Glass noisy chaotic time series prediction by a swarmoptimized
neural network, Journal of Physics:
Conference Series, Volume 720, XIX Chilean Physics
Symposium 201426–28 November, (2014)
Concepción, Chile
[29] P. C. de Lima Silva, H. J. Sadaei, R. Ballini and F. G.
Guimarães, "Probabilistic Forecasting With Fuzzy
Time Series," in IEEE Transactions on Fuzzy
Systems, vol. 28, no. 8, pp. 1771-1784, Aug (2020).
[30] Mackey, Michael C., and Leon Glass. "Oscillation
and chaos in physiological control systems." Science
197.4300 (1977): 287-289.