The Experts below are selected from a list of 390018 Experts worldwide ranked by ideXlab platform
Ching-hsue Cheng - One of the best experts on this subject based on the ideXlab platform.
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fuzzy Time Series Model based on probabilistic approach and rough set rule induction for empirical research in stock markets
Data and Knowledge Engineering, 2008Co-Authors: Hia Jong Teoh, Ching-hsue Cheng, Jrshian ChenAbstract:This study proposes a hybrid fuzzy Time Series Model with two advanced methods, cumulative probability distribution approach (CPDA) and rough set rule induction, to forecast stock markets. To improve forecasting accuracy, three refining processes of fuzzy Time Series are provided in the proposed Model: (1) using CPDA to discretize the observations in training datasets based on the characteristics of data distribution, (2) generating rules (fuzzy logical relationships) by rough set algorithm and (3) producing forecasting results based on rule support values from rough set algorithm. To verify the forecasting performance of the proposed Model in detail, two empirical stock markets (TAIEX and NYSE) are used as evaluating databases; two other methodologies, proposed by Chen and Yu, are used as comparison Models, and two different evaluation methods (moving windows) are used. The proposed Model shows a greatly improved performance in stock market forecasting compared to other fuzzy Time Series Models.
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Trend-Weighted Fuzzy Time-Series Model for TAIEX Forecasting
Lecture Notes in Computer Science, 2006Co-Authors: Ching-hsue Cheng, Tai-liang Chen, Chen-han ChiangAbstract:Time-Series Models have been used to make reasonably accurate predictions in the areas of weather forecasting, academic enrolment and stock price etc... We propose a methodology which incorporates trend-weighting into the fuzzy Time-Series Models advanced by S.M. Chen and Hui-Kuang Yu. By using actual trading data of Taiwan Stock Index (TAIEX) and the enrolment data of the University of Alabama, we evaluate the accuracy of our trend-weighted, fuzzy, Time-Series Model by comparing our forecasts with those derived from Chen's and Yu's Models. The results indicate that our Model surpasses in accuracy those suggested by Chen and Yu.
Chen-han Chiang - One of the best experts on this subject based on the ideXlab platform.
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Trend-Weighted Fuzzy Time-Series Model for TAIEX Forecasting
Lecture Notes in Computer Science, 2006Co-Authors: Ching-hsue Cheng, Tai-liang Chen, Chen-han ChiangAbstract:Time-Series Models have been used to make reasonably accurate predictions in the areas of weather forecasting, academic enrolment and stock price etc... We propose a methodology which incorporates trend-weighting into the fuzzy Time-Series Models advanced by S.M. Chen and Hui-Kuang Yu. By using actual trading data of Taiwan Stock Index (TAIEX) and the enrolment data of the University of Alabama, we evaluate the accuracy of our trend-weighted, fuzzy, Time-Series Model by comparing our forecasts with those derived from Chen's and Yu's Models. The results indicate that our Model surpasses in accuracy those suggested by Chen and Yu.
Bo Hu - One of the best experts on this subject based on the ideXlab platform.
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non homogeneous markov wind speed Time Series Model considering daily and seasonal variation characteristics
IEEE Transactions on Sustainable Energy, 2017Co-Authors: Qinglong Liao, Bo HuAbstract:Wind speed Model lays the foundation of wind power simulation and is crucial to the analysis of wind power integrated into power systems. This paper proposes a non-homogeneous Markov chain (NHMC) wind speed Model that takes the daily and seasonal characteristics of wind speed variation into account. An optimal partition method is adopted to divide the wind speed Time Series into several segments affected by seasonal changes. A seasonal index is introduced before Modeling to reduce the impact of seasonal variation. A Time-related variable is also introduced to describe the daily periodic variation of wind speed. Evaluation on the probability distribution, autocorrelation function, and power spectral density of NHMC Model and commonly used wind speed Models is conducted. Moreover, the number of NHMC states on Model performance is investigated. Simulation results demonstrate that the proposed approach offers excellent fitness on overall statistical properties of the wind speed Time Series.
Fumitake Sakaori - One of the best experts on this subject based on the ideXlab platform.
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Lag weighted lasso for Time Series Model
Computational Statistics, 2012Co-Authors: Heewon Park, Fumitake SakaoriAbstract:The adaptive lasso can consistently identify the true Model in regression Model. However, the adaptive lasso cannot account for lag effects, which are essential for a Time Series Model. Consequently, the adaptive lasso can not reflect certain properties of a Time Series Model. To improve the forecast accuracy of a Time Series Model, we propose a lag weighted lasso. The lag weighted lasso imposes different penalties on each coefficient based on weights that reflect not only the coefficients size but also the lag effects. Simulation studies and a real example show that the proposed method is superior to both the lasso and the adaptive lasso in forecast accuracy.
Kunhuang Huarng - One of the best experts on this subject based on the ideXlab platform.
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a neural network based fuzzy Time Series Model to improve forecasting
Expert Systems With Applications, 2010Co-Authors: Tiffany Huikuang Yu, Kunhuang HuarngAbstract:Neural networks have been popular due to their capabilities in handling nonlinear relationships. Hence, this study intends to apply neural networks to implement a new fuzzy Time Series Model to improve forecasting. Differing from previous studies, this study includes the various degrees of membership in establishing fuzzy relationships, which assist in capturing the relationships more properly. These fuzzy relationships are then used to forecast the stock index in Taiwan. With more information, the forecasting is expected to improve, too. In addition, due to the greater amount of information covered, the proposed Model can be used to forecast directly regardless of whether out-of-sample observations appear in the in-sample observations. This study performs out-of-sample forecasting and the results are compared with those of previous studies to demonstrate the performance of the proposed Model.
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a bivariate fuzzy Time Series Model to forecast the taiex
Expert Systems With Applications, 2008Co-Authors: Kunhuang HuarngAbstract:Fuzzy Time Series Models have been applied to forecast various domain problems and have been shown to forecast better than other Models. Neural networks have been very popular in Modeling nonlinear data. In addition, the bivariate Models are believed to outperform the univariate Models. Hence, this study intends to apply neural networks to fuzzy Time Series forecasting and to propose bivariate Models in order to improve forecasting. The stock index and its corresponding index futures are taken as the inputs to forecast the stock index for the next day. Both in-sample estimation and out-of-sample forecasting are conducted. The proposed Models are then compared with univariate Models as well as other bivariate Models. The empirical results show that one of the proposed Models outperforms the many other Models.
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a type 2 fuzzy Time Series Model for stock index forecasting
Physica A-statistical Mechanics and Its Applications, 2005Co-Authors: Kunhuang Huarng, Huikuang YuAbstract:Most conventional fuzzy Time Series Models (Type 1 Models) utilize only one variable in forecasting. Furthermore, only part of the observations in relation to that variable are used. To utilize more of that variable's observations in forecasting, this study proposes the use of a Type 2 fuzzy Time Series Model. In such a Type 2 Model, extra observations are used to enrich or to refine the fuzzy relationships obtained from Type 1 Models and then to improve forecasting performance. The Taiwan stock index, the TAIEX, is used as the forecasting target. The study period extends over the 2000–2003 period. The TAIEX from January to October in each year is used for the estimation, while that covering November and December is used for forecasting. The empirical analyses show that Type 2 Model outperforms Type 1 Model.
