Fuzzy NN Time Series Forecasting

Background: One forecasting method that is often used is time series forecasting. The development of applied mathematics has encouraged new mathematical findings that led to the birth of new branches of mathematics, one of which is fuzzy. Purpose: The objectives of the study, namely forecasting, fuzzy set, time series, fuzzy time series, fuzzy time series Singh, interval ratio and measurement of accuracy level. Method: This research method applies Chen's fuzzy time series in the section of determining the universe of talk you to the fuzzification of historical data and in the part of forecasting results obtained through a heuristic approach by building three forecasting rules, namely Rule 2.1, Rule 2.2, and Rule 2.3 to obtain better results and affect very small AFER values. As well as making modifications to the interval partition section using interval ratios to be able to reflect data variations. Results: Based on the calculation of AFER values for order 2, order 3, and order 4 respectively obtained at 1.06389%, 0.689368%, and 0.711947%. Therefore, it can be said, Singh's fuzzy time series forecasting method based on the ratio of 3rd-order intervals is better than that of 2nd-order and 4th-order. Conclusion: Based on the results of research and discussion that has been carried out, it can be concluded that Singh's fuzzy time series forecasting method has the same algorithm as fuzzy time series forecasting. Singh's fuzzy time series forecasting method based on interval ratios applies fuzzy time series and Singh forecasting. Singh's fuzzy time series forecasting modification accuracy rate based on interval ratios produces excellent forecasting values according to evaluator average forecasting error rate (AFER).

Triple exponential smoothing is one of the prominent linear models for seasonal time series forecasting. Fuzzy time series forecasting is originated as a new advent for forecasting the data which is imprecise and vague. In this work, we proposed a methodology using both triple exponential smoothing and fuzzy time series. It has the advantage of modelling aspects in linear and non-linear setup. Empirical results with real-world data sets show that the hybrid model is an efficient one based on forecasting accuracy than the component models used individually.KeywordsFuzzy time seriesHybridTriple exponential smoothing

n the literature, there have been a good many different forecasting methods related forecasting problems of fuzzy time series. The main issue of fuzzy time series forecasting is the accuracy of the forecasted values. The forecasting accuracy rate is affected by the length of each interval in the universe of discourse. Thus, it is substantial to determine the length of each interval. In this study, new computational method based on class width to determine interval length is proposed and also used coefficient of variation for time series forecasting. After the intervals are formed, the historical time series data set is fuzzified according to fuzzy time series theory. The proposed model has been tested on the student enrollments, University of Alabama and a real life problem of rice production for containing higher uncertainty. This method was compared with existent methods to determine the effectiveness in terms of the mean square error (MSE) and the average forecasting (AFE). The results are shown that the proposed model can achieve a higher forecasting accuracy rate than the existent models.

Fuzzy time series methods have been applied to social forecasting for over a decade; however, little research has been done to discuss the decision of an optimal fuzzy model for time series. In the paper, we evaluate the forecasting performance of three listed multivariate fuzzy models by comparing forecasting MSE of model. The data obtained from AEROM, Taiwan, includes Taiwan’s exports and foreign exchange rate for models’ test. The algorithm for predictive value of the models has three-stage computation procedure: First, calibrating time series correlation, deciding window base and interval partition; second, solving the static forecasting value of each model; third, comparing the dynamic parameter to impact of the forecasting error. The empirical results indicate that increasing predictor variables has no significant effect on predictive performance of the models; increasing length of interval would not improve the prediction performance of the models. Moreover, Fuzzy model is better for short-term time series forecasting. For forecasting purpose, Heuristic model has best forecasting performance among three fuzzy models. The findings of the paper represent a significant contribution to our understanding of the applicability of fuzzy models to predict.

Fuzzy time series model (FTS) has been widely used to forecast various time series data. When some data are missing, the forecasting accuracy will be greatly reduced. Therefore, a new forecasting method G-FTS is proposed based on the Gaussian mixture model and FTS. First, the distribution of the samples is estimated by using a Gaussian mixture model, the missing data are filled with the estimated distribution, and then a fuzzy time series forecasting is performed. The effectiveness of this method is illustrated by forecasting the monthly inventory data of small and medium-sized industrial enterprises in China. According to the characteristics of industrial data, such as time correlation, fast arrival, and large quantity, a dynamic forecasting method Dynamic-GFTS is proposed. This method is used to forecast the data collected by a textile factory. Simulation results show that Dynamic-GFTS is more effective in actual industrial data.

Time series Forecasting is one of crucial techniques that helps with strategic decision-making and mitigating potential risks –One of which is Weighted fuzzy time series (WFTS). Moreover, the interval length of the WFTS plays a crucial role in its modelization and accuracy in predicting future values. Therefore, this research implements a dual optimization on WFTS, which are (1) Particle Swarm Optimization to find the optimum interval length of the WFTS and (2) a Lagrange quadratic to optimize the weight of the fuzzy interval. In this research, a univariate Average Air Temperature located in Malang is used to perform forecasting model. The dataset is taken from BMKG-Indonesia. This research aims to acquire an optimized interval length on fuzzy time series forecasting, i.e., improving its accuracy by finding the optimal interval length. Based on the result, the proposed dual optimization model outperforms the classical WFTS on forecasting. The proposed model excels based on the evaluation matrix values. It has been noticed also that implementing PSO to find the optimum interval length has improved the accuracy of the classical WFTS. The classical WFTS has MAPE and RMSE of 2.4 and 0.73, respectively, while the proposed dual optimized model has 1.01 and 0.3. This approach identifies the best interval values and provides optimum weights related to each data point, providing solid insights for air temperature forecasting.

In this study, the author presents a new model to deal with four major issues of fuzzy time series (FTS) forecasting, viz., determination of effective lengths of intervals (i.e., intervals which are used to fuzzify the numerical values), repeated fuzzy sets, trend associated with fuzzy sets, and defuzzification operation. To resolve the problem of determination of length of intervals, this study suggests the application of an artificial neural network (ANN) based algorithm. After generating the intervals, the historical time series data set is fuzzified based on FTS theory. In part of existing FTS models introduced in the literature, each fuzzy set is given equal importance, which is not effective to solve real time problems. Therefore, in this model, it is recommended to assign weights on the fuzzy sets based on their frequency of occurrences. In the FTS modeling approach, fuzzified time series values are further used to establish the fuzzy logical relations (FLRs). To determine the trends associated with the fuzzy sets in the corresponding FLR, this article also introduces three trend-based conditions. To deal repeated fuzzy sets and trend associated with them, this study proposes a new defuzzification technique. The proposed model is verified and validated with real-world time series data sets. Empirical analyzes signify that the proposed model has the robustness to deal one-factor time series data sets very efficiently than existing FTS models. Experimental results show that the proposed model also outperforms over the conventional statistical models.

In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

The arising air pollution has addressed much attention globally due to its detrimental effects on human health and environment. As an early warning system for air quality control and management, it is important to provide precise information about the future concentrations in pollutants. We present here a time series model in predicting the Air Pollution Index (API) from three different stations; industrial, residential, and sub-urban areas between 2000 and 2009. In this paper, the Box–Jenkins approach of seasonal autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and three models of fuzzy time series (FTS) have been compared by using the mean absolute percentage error, mean absolute error, mean square error, and root mean square error. Although all the methods were used as operational tools, the ANN seemed more accurate in forecasting API. The results showed that FTS (i.e. Chen’s, Yu’s, and Cheng’s) performed inconsistent results since the conventional methods of ARIMA outperformed the performance of FTS. However, consistent results were achieved as the ANNs gave the smallest forecasting error compared to FTS and ARIMA.

About the research of fuzzy time series model are proposed by Song and Chissom(1993). There are many researchers have proposed many different methods and models. Example the p-order fuzzy time series model FAR(p), the p-order fuzzy time series model with fuzzy trend, and the p-order fuzzy seasonal time series model FSAR(p)s. But, about a fuzzy time series has shown a tendency to increase or decrease over a particular period and also to follow a seasonal pattern, FASRI(p, d)×(P, D)(subscript s) that is no any paper to research this topic. In this research, we want to study the FASRI(p, d)×(P, D)(subscript s) model of fuzzy time series. First, we make testing for the fuzzy trend of fuzzy time series by the rule of Liaw(2000) proposed. If the fuzzy time series with fuzzy trend, we make a differencing for the original time series, then make testing for the fuzzy seasonal of the new fuzzy time series by the rule of Liaw(2000) proposed. If the new fuzzy time series with fuzzy seasonal. It is told us the fuzzy time series simultaneous with fuzzy trend and fuzzy seasonal. We must consider the fuzzy time series simultaneous with fuzzy trend and fuzzy seasonal at the model construction. Finally, we use two relevant instances verify our propose method.

A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper)rectangles and propose efficient in-memory processing and secondary memory pruning techniques for RNN queries in both 2D and high-dimensional spaces. These techniques are generalized for kRNN queries, which return the k nearest neighbors for every point in the range. In addition, we devise an auxiliary solution-based index EXO-tree to speed up any type of NN query. EXO-tree is orthogonal to any existing NN processing algorithm and, thus, can be transparently integrated. An extensive empirical study was conducted to evaluate the CPU and I/O performance of these techniques, and the study showed that they are efficient and robust under various data sets, query ranges, numbers of nearest neighbors, dimensions, and cache sizes.

The aim of this paper is to present a detailed step by step method for classification of high resolution satellite imagery into defined classes such as high dense vegetation, water, wet land, Agricultural land etc., using fuzzy rule set. In this area, object based analysis is used for image classification. An Objectoriented approach isbased on the segmentation that is being followed for information extraction of variety of thematic information from high-resolution satelliteimagery. In this method, fuzzy rule set is defined for every class based on the class hierarchy; every defined class is assigned associated segments with high similarity degree, using eCognition Software. Object-oriented classification rule set provide a complete accurate and efficient method of information extraction. In compared the object-oriented with traditional pixel-based classification and proved that object-oriented method has provided high accuracy classification. Nearest Neighbor (NN) classification method was used to discriminate the land cover classes. The overall accuracy is obtained 99.99% and kappa coefficient obtained 99% for the satellite image of Landsat-8. Keyword: Classification, eCognition, Fuzzy rule based, High resolution satellite image, Segmentation.

Aiming at a large number of ambiguous, imprecise and incomplete data in the real world, fuzzy time series has come into being and developed into an effective forecasting approach. In the process of modeling and forecasting of fuzzy time series, the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different lengths. In this paper, a forecasting approach for fuzzy time series, which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting, is proposed to solve the problem of time series forecasting with high forecasting precision. In the proposed approach, in order to describe the fuzzy logic relationship and fuzzy trend of historical data, we first do differential processing on the historical samples. Then, Fuzzy C-means (FCM) clustering algorithm is used to generate several partition intervals tentatively. In the sequel, we use the principle of justifiable granularity to constantly adjust the width of all the intervals, so that these information granules associated with corresponding intervals become the most "informative" information granules. Finally, the boundary of information granules is used as the basis of interval division to complete the forecasting task. An illustrative example is provided to demonstrate the essence of the proposed approach. The comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.

To ensure the sustainable growth of coal production, it is necessary to analyse the growth, to ground the plans and management decisions on effective diagnostics and prediction of current and future situation at the coal production. This study presents a application of fuzzy time series forecasting methods, The new technique is applied to forecasting the coal production data using a fuzzy approach. For testing the methodology, statistical data on the coal production from 1980- to 2019. The Sturges rule is proposed to use as the universe of discourse. The intervals of variation of such indicators as growth rate are calculated when applying the approach to all defined fuzzy sets. The ARIMA model algorithm was applied by using the R - software to find the forecasted values. The forecasting results, obtained by the fuzzy time series method, are supposed to have more accuracy rate than time series model.

Turbo Similarity Searching (TSS) is the simplest and most recent chemical similarity searching (SS) approach, which improves the effectiveness of SS by performing a multi-target searching. TSS has four important elements, namely structural representation, similarity coefficient, number of nearest neighbours (NNs), and fusion rule, and any changes in these elements could affect the TSS results. A previous study suggested the advantage of using large numbers of reference compounds with small fractions of the database structures to obtain a better recall in group fusion. Therefore, this study aims to investigate the effect of partial ranking on TSS utilising different fusion rules and different numbers of NNs on the ChEMBL database and to evaluate whether these observations hold in TSS. Furthermore, the objective is to observe the effect of the indirect relationship feature of TSS on the partial ranking investigation. The results showed that the effect of using partial ranking on TSS was significant. This study also found that the performance of TSS improved as the database proportions used in the fusion process decreased and by using a small number of NNs. In addition, fusion rules based on reciprocal rank positions (RKP), maximum similarity score (sMAX), and sMNZ were superior to all the other fusion rules.