High order fuzzy time series method based on pi-sigma neural network

Baş, Eren; Groşan, Crina; Eğrioğlu, Erol; Yolcu, Ufuk

doi:10.1016/j.engappai.2018.04.017

Cited by 61 publications

(23 citation statements)

References 46 publications

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“…Figure 5 shows the prediction results in 2018 by using the PSI, MA, ES, and ARIMA methods. As shown in Figure 5, between January 2018 and May 2018, the predicted VS using the PSI method can be almost identical with actual VS, comparing to the MA, ES, and ARIMA methods, and a lower MAPE of 4.48% is also achieved by Equation (18). Meanwhile, it can be seen that, the trend of the predicted values between June 2018 and December 2018 is similar to that of the actual values when using PSI method, but the difference between them at each time point is larger than that of using the MA, ES, and ARIMA methods, achieving a value of MAPE at 19.36% for the period between June 2018 and December 2018.…”

Section: Periodic Recognition and Prediction On Volkswagen Sales (Vs)mentioning

confidence: 77%

“…It is also possible to hybrid different methods to improve overall forecasting accuracy [15]. However, no traditional forecasting methods can meet all the targets [16][17][18], while applying heuristic methods are also worth researching [19]. Here, a period-sequential index algorithm with a sigma-pi neural network (SPNN-PSI) is proposed and dedicated to the prediction of time series data.…”

Section: Period-sequential Index (Psi) Algorithmmentioning

confidence: 99%

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A New Period-Sequential Index Forecasting Algorithm for Time Series Data

et al. 2019

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A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets. It is also concluded that: There is a trend that the higher the correlation coefficient value of the reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (>0.4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.

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Section: Periodic Recognition and Prediction On Volkswagen Sales (Vs)mentioning

confidence: 77%

Section: Period-sequential Index (Psi) Algorithmmentioning

confidence: 99%

A New Period-Sequential Index Forecasting Algorithm for Time Series Data

et al. 2019

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“…Based on the sample set that is collected through the simulation of the predesigned fuzzy PID control system under various conditions of hoist brakes, the parameters above are obtained through training. 9–11 Figures 4 and 5 show the trained membership function of deceleration error and the change rate of deceleration error, as well as the change amount of proportional coefficient Δ K p , integral coefficient Δ K i , and differential coefficient Δ K d .…”

Section: Training Of the Fuzzy Neural Networkmentioning

confidence: 99%

Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

Tian

Xiao

et al. 2020

Advances in Mechanical Engineering

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In comparison with constant torque brakes, constant deceleration brakes are more advantageous for the safety of mining hoists, but complete set of such products manufactured by big companies are not what ordinary mining enterprises can afford. As an alternative solution, this article develops a constant deceleration compensation device, which adds the function of constant deceleration brake onto the original brakes. Control strategy based on Fuzzy Neural Network PID is investigated and simulated with the combination of AMEsim and Simulink. An actual device is built and tested in real industrial field. The application illustrates the feasibility of this constant deceleration compensation device, which can achieve constant decelerations within a very short time. This device will prevent dangerous decelerations from happening to hoists at a much lower cost, and greatly improve the safety and reliability of mining hoists.

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“…Traditionally, the rule-based methods have been used to build FLRs [29]. However, to reduce the computational complexity and improve the forecast accuracy, authors have used ANNs [31,32], fuzzy inference systems [33], support vector regression [34], and a general regression neural networks [35] as alternatives. For a multi-factors FTS, Chen and Chen [36] proposed a leverage of fuzzy variations between the main factor and the secondary factor to forecast the TAIEX.…”

Section: Fuzzy Time Seriesmentioning

confidence: 99%

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

2019

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Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

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High order fuzzy time series method based on pi-sigma neural network

Cited by 61 publications

References 46 publications

A New Period-Sequential Index Forecasting Algorithm for Time Series Data

A New Period-Sequential Index Forecasting Algorithm for Time Series Data

Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

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