Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system

Lin, Chin‐Tsai; Lee, In-Fun

doi:10.1016/j.eswa.2008.10.057

Cited by 16 publications

(11 citation statements)

References 18 publications

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“…To take long-term operation into consideration, FMGM(1,1) has been successfully applied to predict the turning time of stock markets [40]. Recently, MFGM(1,1) has also presented excellent predictive accuracy in the case of forecasts with short-term historical and randomly fluctuating data [41]. From these obtained results, the MFGM(1,1) model predicted accurately under an instable environment, randomly fluctuating data and a limited data sample.…”

mentioning

confidence: 61%

“…Thus, it is difficult to identify which are better models. Fortunately, as the notes of [41], the Fourier correction approach is to increase prediction capability from the considered input data set and does not change the local characteristics of grey model prediction. Thus, it can be concluded that FMGM models have obtained high performance compared with other prediction models.…”

Section: Resultsmentioning

confidence: 99%

“…This model has been successfully applied, with high accuracy, compared to the traditional GM(1,1) model [29,40,41]. However, the combination of Fourier series and Markow chain with DGM(2,1) and Verhulst method have not yet been researched until now.…”

Section: Fourier Markov Grey Modified Model (Fmgm)mentioning

confidence: 99%

“…Firstly, the residual errors from the predicted series of three traditional grey models consisting of GM(1,1), DGM(2,1), and Verhuslt model are adopted. After that, a Fourier-corrected approach and Markov chain are integrated into these grey models in the same way as [29,40,41,49,50]. Finally, the performance of the proposed models is compared with traditional grey models and FMGM(1,1) to collect the best model which is capable of analyzing the trend of the Taiwan e-paper industry.…”

Section: Fourier Markov Grey Modified Model (Fmgm)mentioning

confidence: 99%

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Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Huang¹,

Wang²,

Dang³

et al. 2015

Sustainability

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Electronic paper (e-paper) is a major sector of Taiwan's Optoelectronic industry. It has paid much attention on the development of flexible displays. Even though the market is booming, the future is still unclear for business development. No research has yet forecasted the future market size of the e-paper industry. In addition, proposing an appropriate forecasting model to understand the trend of this industry plays a crucial role for market players and government's authorities in formulating correct strategies. Therefore, in this paper, the future market size of Taiwan's e-paper industry is predicted by an effective combined grey model. Two combinations of DGM(2,1) and Verhulst model with Fourier series and Markov chain, namely FM-Verhulst and FMDGM(2,1), were presented. Based on the annual data of Taiwan's e-paper industry, the results show that the forecasting performances of two FM-Verhulst and FMDGM(2,1) models are highly accurate compared with other grey models. Precision is 96.36% and 97.77%, respectively. However, for long-term prediction, the FMDGM(2,1) model obtains the best performance in all proposed grey models. With obtained forecasting results in Taiwan's e-paper industry by the FMDGM(2,1) model, it can be pointed out that the future market size of Taiwan's e-paper would slowly increase in the next few years.

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mentioning

confidence: 61%

Section: Resultsmentioning

confidence: 99%

Section: Fourier Markov Grey Modified Model (Fmgm)mentioning

confidence: 99%

Section: Fourier Markov Grey Modified Model (Fmgm)mentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Huang¹,

Wang²,

Dang³

et al. 2015

Sustainability

View full text Add to dashboard Cite

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“…The model has been tested on both stationary and nonstationary time series, and the experiments show that in both cases the adaptive ensemble method leads to a prediction accuracy comparable to the best methods. For more detailed information see [16] [17].…”

Section: Adaptive Elmmentioning

confidence: 99%

Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm

Hosseinioun

2016

JMF

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In financial field, outliers represent volatility of stock market, which plays an important role in management, portfolio selection and derivative pricing. Therefore, forecasting outliers of stock market is of the great importance in theory and application. In this paper, the problem of predicting outliers based on adaptive ensemble models of Extreme Learning Machines (ELMs) is considered. We found out that the proposed model is applicable for outlier forecasting and outperforms the methods based on autoregression (AR) and extreme learning machine (ELM) models.

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Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting

Tang

Liao

2018

Artificial Intelligence on Fashion and Textiles

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Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system

Cited by 16 publications

References 18 publications

Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Forecasting Outlier Occurrence in Stock Market Time Series Based on Wavelet Transform and Adaptive ELM Algorithm

Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting

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