5,996 research outputs found
The cross-association relation based on intervals ratio in fuzzy time series
The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modifed steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals
River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques
In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values
A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases
Updating known distribution models for forecasting climate change impact on endangered species
To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their
distributional response to climate change, especially under the current situation of rapid change. However, these
predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard
of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of
known species distribution models, but proceeding to update them with the variables yielded by climatic models before
projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered
Bonelli’s Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to
a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that
the main threat for this endangered species would not be climate change, since all forecasting models show that its
distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of
linking conservation biology with distribution modelling by updating existing models, frequently available for endangered
species, considering all the known factors conditioning the species’ distribution, instead of building new models that are
based on climate change variables only.Ministerio de Ciencia e Innovación and FEDER (project CGL2009-11316/BOS
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Variable grouping in multivariate time series via correlation
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho
Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations
Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.
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