Fuzzy time series forecasting of nonstationary wind and wave data

Stefanakos, Christos

doi:10.1016/j.oceaneng.2016.05.018

Cited by 38 publications

(23 citation statements)

References 42 publications

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“…Especially, artificial intelligence is used in most forecast problems due to its performance for nonlinear systems. In nonlinear or nonstationary time series, many studies on adaptive neuro-fuzzy models (ANFIS) have been proposed, such as those by Su and Cheng [25] and Stefanakos [26].…”

Section: Fuzzy Time Seriesmentioning

confidence: 99%

“…In the soft computing techniques, artificial neural networks (ANNs) are used in most cases due to their performance in nonlinear systems. To deal with the problem of nonlinear or nonstationary time series, many notable studies on adaptive neuro-fuzzy models (ANFISs) have been done by Su and Cheng [25], Stefanakos [26], and others. Therefore, the motivation for this study is based on three factors used to fit a linear combination of multifactor fuzzy time-series to forecast the stock index, because a linear model is simple and easily explained.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Fuzzy Time Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…To improve the stock index forecasts, Rubio et al proposed a new weighted fuzzy-trend time series method that proved more superior than other models [58]. Further, Stefanakos et al first applied fuzzy time series forecasting in wave field predictions which supposed to be a satisfying application for nonstationary series [59]. For wind speed series forecasting, fuzzy logic also has excellent performance [32].…”

Section: Definition Of Fuzzy Time Seriesmentioning

confidence: 99%

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

Wang

2018

Applied Soft Computing

110

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With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution.

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“…In the big data era, a large number of time series data are continuously generated in the network systems, such as stock price, sales volume, production capacity, weather data, ocean engineering, engineering control, and largely in any system of applied science and engineering which involves investigations of time-varying parameters [1][2][3]. In general, the distribution of time series data changes over time, and is non-stationary [4,5], while some data shows potential periodicity characteristics.…”

Section: Introductionmentioning

confidence: 99%

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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Fuzzy time series forecasting of nonstationary wind and wave data

Cited by 38 publications

References 42 publications

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

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

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

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

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