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A brief review of modeling approaches based on fuzzy time series

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Abstract

Recently, there seems to be increased interest in time series forecasting using soft computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set (RS) and evolutionary computing (EC). Among them, fuzzy set is widely used technique in this domain, which is referred to as “Fuzzy Time Series (FTS)”. In this survey, extensive information and knowledge are provided for the FTS concepts and their applications in time series forecasting. This article reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June). Here, we also provide a brief introduction to SC techniques, because in many cases problems can be solved most effectively by integrating these techniques into different phases of the FTS modeling approach. Hence, several techniques that are hybridized with the FTS modeling approach are discussed briefly. We also identified various domains specific problems and research trends, and try to categorize them. The article ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

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Notes

  1. References are: [10, 29, 30, 39, 40, 50, 51, 54, 57, 73, 84, 95, 105, 106, 110, 111, 116, 117, 132, 165, 170].

  2. References are: [14, 18, 34, 35, 70, 74, 144–146, 159].

  3. References are: [2, 3, 6, 7, 19, 22, 23, 26, 32, 57, 80, 122, 138–141, 153].

  4. References are: [18, 19, 31, 36, 41, 68, 70, 74, 79, 80, 93, 98, 109, 128, 133, 135, 137, 140, 141, 171].

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  1. School of Mathematics and Computer Applications, Thapar University, Patiala 04, Patiala, India

    Pritpal Singh

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Appendix

Appendix

  1. 1.

    Abbreviations used in Sect. 3:

    • SLFF: Single-layer feed forward

    • MLFF: Multi-layer feed forward

    • FFNN: Feed-forward neural network

    • BPNN: Back-propagation neural network

    • GA: Genetic algorithm

    • GP: Genetic programming

  2. 2.

    Abbreviations used in Sect. 4:

    • ANN: Artificial neural network

    • GA: Genetic algorithm

    • EC: Evolutionary computing

    • PSO: Particle swarm optimization

    • HMM: Hidden-markov model

    • AE: Adaptive expectation

    • IFS: Intuitionistic fuzzy set

    • SOFM: Self-organizing feature maps

    • FFNN: Feed-forward neural network

    • CPDA: Cumulative probability distribution approach

    • FCM: Fuzzy c-mean

    • LEM2: Learning from example module 2

    • TAIFEX: Taiwan futures exchange

    • TWSI: Taiwan weighted stock index

    • SARIMA: Seasonal autoregressive integrated moving average

    • HMM: Hidden-markov model

    • IFS: Intuitionistic fuzzy set

    • ARIMA: Autoregressive integrated moving average

    • VARMA: Vector autoregressive moving average

  3. 3.

    Abbreviations used in Sect. 5:

    • TAIEX: Taiwan stock exchange capitalization weighted stock index

    • TAIFEX: Taiwan futures exchange

  4. 4.

    Abbreviations/symbols used in Section 6:

    • \(\bar{A}\): Mean

    • \(U\): Theil’s Statistic

    • \(TS\): Tracking Signal

    • \(DA\): Directional Accuracy

    • \(\delta _r\): Evaluation Parameter

    • \(R\): Correlation Coefficient

    • \(R^2\): Coefficient of Determination

    • \(PP\): Performance Parameter

    • AFER: Average Forecasting Error Rate

    • MAPE: Mean Absolute Percent Error

    • RMSE: Root Mean Square Error

    • MSE: Mean Square Error

  5. 5.

    Abbreviations used in Sect. 7:

    • TAIFEX: Taiwan futures exchange

    • TAIEX: Taiwan stock exchange capitalization weighted stock index

    • PVMI: Production value of the machinery industry in Taiwan

    • MSSD: Monthly sales of soft drinks

    • ID: Inventory demand

    • ITPE: IT project expenditures

    • TSMC: Taiwan semiconductor manufacturing company

    • HSI: Heng seng index

    • WP: Wheat production

    • TTUSA: Taiwan tourists to the USA

    • LCP: Lahi crop production

    • FOREX: Foreign exchange market

    • OPV: Outpatient visits

    • AQPP: Australian quarterly power production

    • MG: Mackey-Glass

    • DOC: Daily Ozone Concentration

    • DTDST: Daily Temperature Data of Taipei

    • DCDDST: Daily Cloud Density Data of Taipei

    • TWSI: Taiwan Weighted Stock Index

    • TSEC: Taiwan Stock Exchange Corporation

    • NTD: New Taiwan dollar

    • KOSPI: Korea composite stock price index

    • YRAC: Yearly road accident casualties

    • TSI: Taiwan stock index

    • NYSE: New York stock exchange composite index

    • NASDAQ: National association of securities dealers automated quotations system

    • IMKB: Index 100 in stocks and bonds exchange market of Istanbul

    • PG: Patient Granted

    • TE: Taiwan export

    • MR: Monthly Rainfall

    • DT: Daily Temperature

    • DCD: Daily Cloud Density

    • SBI: State bank of India

    • SCI: Shanghai composite index

    • ANSO: Seasonal time series data of sulfur dioxide

    • Series G: Monthly passenger data travel in international air

    • TTD: Taiwan’s tourism demand

    • ICT: Diffusion of information and communication technologies

    • IKD: Iran khordo diesel

    • IK: Iran khordo

    • LMA: Levenberg-Marquardt algorithm

    • RPP: Rice production of Pantnagar

    • MLP: Multilayer Perceptron

    • AR: Autoregressive

    • MA: Moving average

    • ARMA: Autoregressive moving average

    • VARMA: Vector autoregressive moving average

    • ARIMA: Autoregressive integrated moving average

    • SARIMA: Seasonal autoregressive integrated moving average

    • HMM: Hidden-markov model

    • LRM: Linear regression model

    • MSVPMC: Monthly sales volume of propelynes manufacturing company

    • GDCI: Gross domestic capital of India

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Singh, P. A brief review of modeling approaches based on fuzzy time series. Int. J. Mach. Learn. & Cyber. 8, 397–420 (2017). https://doi.org/10.1007/s13042-015-0332-y

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