A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships

Abhishekh,; Gautam, Surendra Singh; Singh, S. R.

doi:10.1142/s0218488519500120

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…However, the authors point out that the integration of neural networks through the ANFIS model significantly reduces the interpretability of the created system. Researches [17,18] apply triangular fuzzy functions to the moving average prediction and the obtained forecast is implemented on the share at Bombay Stock Exchange. Paper [19] develops a fuzzy trading system integrating technical indicators on the basis of which they predict the development of the stock index.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Type Problem [16] Gaussian T1FS stock index prediction [23] Triangular T2FS stock index prediction [17] Triangular T1FS stock index prediction [18] Triangular T1FS stock index prediction [19] Trapezoidal T2FS stock index prediction [20] Triangular T1FS stock index prediction [22] Bell ANFIS stock index prediction [21] Bell T1FS stock prediction [24] Triangular T1FS; T2FS stock index prediction [25] Triangular T2FS profit prediction [26] Triangular T2FS stock index prediction For this reason, the authors of this paper consider this area to be very important, especially a detailed examination of membership functions in terms of not only their appropriate shape (T1FL and T2FL) resulting from the nature of the selected dataset, but also the appropriate degree of uncertainty between dual membership functions (T2FLS) resulting from the nature of the stock market.…”

Section: Author Fuzzy Setsmentioning

confidence: 99%

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Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

Janková

Rakovská

2022

Applied Sciences

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This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA of the US market. Type-2 Fuzzy Logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Author Fuzzy Setsmentioning

confidence: 99%

Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

Janková

Rakovská

2022

Applied Sciences

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“…Later, Seasonal Autoregressive Integrated Moving Average (SARIMA) was introduced to incorporate ARIMA and seasonal variation [11], while the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) was introduced to include the long-run effect of external variables in the ARIMA model [12]. However, these linear models are insufficient to predict nonlinear components and the uncertainty and volatility of time series, leading to several errors [13]. Nonlinear time series models which use nonlinear functions in prediction, such as Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN), have been used to handle nonlinear components of time series data [11,12].…”

Section: Introductionmentioning

confidence: 99%

Investigation of a Suitable Hybrid Time Series Model for Predicting Clove Price

Ali,

Mushi

2023

JICTS

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The prediction of clove prices in domestic markets is affected by non-linear factors, including the monopoly market operational environment. Most of the hybrid time series models used in the prediction of crop prices do not consider the monopoly market operational environment as a nonlinear factor. This study investigated a suitable hybrid time series model for predicting clove price under the monopoly market in Zanzibar, Tanzania. The study conducted desk reviews on existing hybrid time series models and realize that ARIMA-ANN, ARIMA-SVM, ARIMAX-ANN, and SARIMA-NARNN are the most common and efficient models that have been employed to predict crop prices. Based on identified models, the study performed several experiments to investigate the accuracy of these models on predicting clove prices with the monopoly market operational environment as a nonlinear factor. The mean absolute percentage error (MAPE) was used as a performance metric. The monthly average prices of cloves from January 2007 to December 2019 were used to utilise these experiments. Results show that the ARIMA-SVM (MAPE = 0.45%) outperformed the ARIMA-ANN model (MAPE = 0.48%) in predicting clove prices under a monopoly operational market. The study recommended future research to investigate hybrid models for predicting production and planted areas of cloves.

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“…To handle the seasonality of time series, researchers have built the seasonal ARIMA [14] based on the ARIMA model. Besides, since the uncertainty and volatility of time series bring several errors for time series prediction [15], many time series prediction models [16]- [18] based on the fuzzy time series [19] have been proposed due to the effectiveness of dealing with uncertainty [20], [21] by fuzzification. In recent years, due to the ability of machine learning methods to use information efficiently, some of them, such as neural networks [22]- [24] and random forest [25], have been widely used in time series prediction models.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Zhao

Mo²,

Deng

2020

IEEE Access

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Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity. INDEX TERMS Complex network, time series forecasting, node degree, visibility graph, variation trend.

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A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships

Cited by 7 publications

References 30 publications

Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

Investigation of a Suitable Hybrid Time Series Model for Predicting Clove Price

An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

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