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Fuzzy time series has been utilized to solve the problem of time series prediction in diverse fields. In the fuzzy time series prediction, the fuzzy logical relations have great impacts on the prediction performance. To obtain the exact and complex fuzzy logical relations between the fuzzy variables for the time series prediction, we propose to extract them from the historical data and model them...
The study is devoted to the clustering of granular data and an evaluation of the results of such clustering. A comprehensive and systematic approach is developed, which is composed of three fundamental phases: 1) representation of granular data; 2) clustering carried out in the representation space of information granules; and 3) evaluation of quality of clusters following the reconstruction criterion...
The traditional financial time series forecasting methods use accurate input data for prediction, and then make single-step or multi-step prediction based on the established regression model. So its prediction result is one or more specific values. But because of the complexity of financial markets, the traditional forecasting methods are less reliable. In this paper, we transform the financial time...
The Asian steel price index is a leading indicator of the Asian economy. Studying the collection of Asian steel price index data and establishing a fuzzy time series model used to analyze the relationship between the remuneration of the Asian steel price index rate of change in the future. The results of the analysis are as follows. (1) The model showed that the predictive value of the 2012 Asian...
Study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improve forecasting accuracy. Recently, Chen, et al [12] has proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's...
This paper is aimed at improving the forecasting accuracy with correcting two deficiencies, subintervals failing to well represent the data distribution structures and a single antecedent factor in the fuzzy relationships in current fuzzy time series models. First, the universe of discourse is partitioned into subintervals with the midpoints of two adjacent cluster centers generated by the fuzzy clustering...
In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series, where the main factor is the TAIEX and the secondary factors are either the Dow Jones, the NASDAQ, the M1b (Taiwan), or their combinations. First, we fuzzify the historical data of the main factor into fuzzy sets with a fixed length of intervals to form...
In this paper, we present a new method to forecast the Taiwan stock exchange capitalization weighted stock index (TAIEX) based on high-order fuzzy logical relationships. First, the proposed method fuzzifies the historical data into fuzzy sets to form high-order fuzzy logical relationships. Then, it calculates the value of the variable between the subscripts of adjacent fuzzy sets appearing in the...
In this paper, Taiwan stock index is forecasted by use of optimized type 2 fuzzy time series concept. In type 2 fuzzy time series, more observation can be used for forecasting. Thus, this model is closer to reality than type1 model.
Fuzzy time series models has been applied to forecast various problems and have been shown to forecast better than other models. In this article, we intend to apply Chou and Lee's fuzzy time series model to forecast the Baltic Dry Index (BDI) index for the next month. The root mean square error is one criteria to evaluate the forecasting performance. Empirical results show that the fuzzy time series...
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