Fuzzy Time Series Analysis Research Articles

Trend and Fuzzy Time Series Analysis of Live Births Registration in Northern Ghana

Abstract This study investigated four trend analysis models namely; linear, quadratic, semi log linear and semi log quadratic to study the pattern of live births registration in the Northern Region of Ghana. The study revealed that Semi log linear trend model is the best trend model for studying the pattern of live births registration in the Northern Region of Ghana based on AIC and BIC criteria. The study further fitted four existing fuzzy time series (FTS) models for forecasting live births registration in the Northern Region of Ghana. The Chen, Singh, Heuristic and Chen-Hsu models are the four models used to analyze the data.

Turning Point Induced Knowledge Forecasting under Uncertainties (TrIK)

Time series forecasting is used in many applications like price prediction, stock trend analysis, etc., This forecasting is dependent on multiple variables, which have an impact on contextual uncertainties. The inability to handle contextual uncertainty, induces incorrect predictions, leading to crucial business decision-making failures. This further restricts the abilities of forecasting and decision-making in a dynamic and partially observable real-life environment. This paper proposes a Turning Points-based approach along with shoulder impact processing. This imparts uncertainties in learning and handles such scenarios more scientifically and gracefully. Turning points are predicted based on probable impending surprise. These turning points are forecasted based on uncertainties found in the variables contributing to sudden out-of-the-pattern changes in the next cycle of forecasting. The shoulder impact areas are classified based on trend changes to improve decisions. Multivariate analysis, fuzzy time series analysis, and contextual impact determination assist to identify such uncertainties and inter-dependencies for improved forecasting. These points support trend forecasting instead of forecasting at an instance. This helps in many applications including decision-making regarding pricing, storing, and distribution of short-span, medium-span, and long-term span perishable commodities. TrIK can be extended to multiple time series forecasting applications to reduce wastage and economic loss.

MODELING OF CO2 EMISSION STATISTICS in TURKEY BY FUZZY TIME SERIES ANALYSIS

The process of determining the values which a time series will receive in the future is a very important concept. The fuzzy time series method has been widely used in recent years as it is more convenient to process data in small samples which are incomplete and/or ambiguous, and it does not contain any assumptions for time series. In this study, fuzzy time series analysis was used to predict CO2 emission values for Turkey. For this purpose, time series (annual) for total greenhouse gas emissions by sectors (CO2 equivalent) between 1990 and 2016 were analyzed. The main goal of this study is to model greenhouse gas emission statistics in Turkey with fuzzy time series analysis.

Using Fuzzy Models and Time Series Analysis to Predict Water Quality

Using Fuzzy Models and Time Series Analysis to Predict Water Quality

Fuzzy time series for real-time flood forecasting

This study applied fuzzy time series (FTS) analysis to develop a real-time flood forecasting model to forecast typhoon flood discharges. Two crucial factors that influence the performance of FTS are the partition of intervals of the variable and the defuzzification method. This study examined the effects of various interval lengths and two defuzzification methods, the centroid and the midpoint methods, on the model performance. Criteria of model completeness and consistency principle were used to determine the effective interval length, and analytic results showed that the midpoint method outperforms the centroid method. Two structures of forecasting models were proposed to make multiple-hour-ahead flood forecasts. Validation results from typhoon flood events in the Wu River in Taiwan showed that the proposed FTS model, which is novel in hydrologic forecasting, can effectively forecast flood discharges.

Online Failure Prediction for Railway Transportation Systems Based on Fuzzy Rules and Data Analysis

Nowadays, software systems have been more and more complex, which causes great challenges to maintain the availability of the systems. Online failure prediction provides an effective approach to guaranteeing the validity of the systems. Most of the current technologies for online failure prediction require some prior knowledge, such as the model of the system or failure patterns. This paper proposes a new method based on fuzzy rules and time series analysis. Specifically, fuzzy rules are used to model the relationships among different variables, whereas univariate time series analysis is used to describe the evolution of each variable. Thus, for a dependent variable, we have two predicted values: one is from the time series model, and the other is computed from fuzzy rules with fuzzy inference. If the difference between the two values exceeds a threshold, then we declare that there would be a failure in some time period ahead. Different from the existing methods, the proposed method considers not only the evolutionary trend of each variable but also the relationships among different variables. Moreover, we do not need any prior knowledge such as system model or failure patterns. We use a railway transportation system as an example to illustrate our method.

High order fuzzy time series method based on pi-sigma neural network

Fuzzy time series methods, which do not require the strict assumptions of classical time series methods, generally consist of three stages as fuzzification of crisp time series observations, determination of fuzzy relationships and defuzzification. All of these stages play a very important role on the forecasting performance of the model. An important stage of the fuzzy time series analysis is to determine the fuzzy relationships. Artificial neural networks seem to be very effective in determining fuzzy relationships that improve the accuracy of the forecasting performance. Several neuron models with different characteristics have been proposed so far. One of these models is Pi-Sigma neural network. An important advantage of Pi-Sigma neural network is that it requires fewer weights and nodes and has a lower number of computations when compared to multilayer perceptron. In this study, a new model for determining the fuzzy relationships for high order fuzzy time series forecasting which uses Pi-Sigma neural network is introduced. A modified particle swarm optimization model is used to train the Pi-Sigma network. We test the new model on two real datasets and we also perform a simulation study. The results are compared to the ones obtained by other techniques and show a better performance.

Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process

All fuzzy time series approaches proposed in the literature consider three steps constituting the solution process as separate processes. Thus, model error is the sum of the errors that may occur in each step. In this regard, synchronous evaluation of the steps constituting the analysis process will produce a single model error and will lead to a reduction in the model error. Within the scope of this study, we proposed an approach which evaluates the steps constituting fuzzy time series analysis in one process synchronously to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index. In the proposed approach, defuzzification step is eliminated by using real values of time series as target values in the identification of fuzzy relations step. In this respect, determination of fuzzy cluster centres in fuzzification and the training of artificial neural network with single multiplicative neuron which is used the identification of fuzzy relation are carried out in a single optimization process with particle swarm optimization. This work also covers comprehensive literature review and summary info of related methodologies including fuzzy time series, particle swarm optimization, single multiplicative neuron model and fuzzy C-means clustering. The proposed method is applied to twelve different time series and a total of twenty-four measurements are done and superior forecasting performance of the method is proven.

A better project performance prediction model using fuzzy time series and data envelopment analysis

Earned value management (EVM) is a critical project management methodology that evaluates and predicts project performance from cost and schedule perspectives. The novel theoretical framework presented in this paper estimates future performance of a project based on the past performance data. The model benefits from a fuzzy time series forecasting model in the estimation process. Furthermore, fuzzy-based estimation is developed using linguistic terms to interpret different possible conditions of projects. Eventually, data envelopment analysis is applied to determine the superior model for forecasting of project performance. Multiple illustrative cases and simulated data have been used for comparative analysis and to illustrate the applicability of theoretical model to real situations. Contrary to EVM-based approach, which assumes the future performance is the same as the past, the proposed model can greatly assist project managers in more realistically assessing prospective performance of projects and thereby taking necessary and on-time appropriate actions.

A novel forecasting method based on multi-order fuzzy time series and technical analysis

Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear dynamic systems. It is a challenge to develop the inherent rules using the traditional time series prediction technique. In this paper, we proposed a new forecasting method based on multi-order fuzzy time series, technical analysis, and a genetic algorithm. Multi-order fuzzy time series (first-order, second-order and third-order) are applied in the proposed algorithm, and to improve the performance, genetic algorithm is used to find a good domain partition. Technical analysis such as the Rate of Change (ROC), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (KDJ) are introduced to construct multi-variable fuzzy time series, and exponential smoothing is used to eliminate noise in the time series. In addition to the root mean square error and mean square error, the directional accuracy rate (DAR) is also used in our empirical studies. We apply the proposed method to forecast five well-known stock indexes and the NTD/USD exchange rates. Experimental results demonstrate that our proposed method outperforms other existing models based on fuzzy time series.

An improved fuzzy time-series method of forecasting based on L–R fuzzy sets and its application

Classical time-series theory assumes values of the response variable to be ‘crisp’ or ‘precise’, which is quite often violated in reality. However, forecasting of such data can be carried out through fuzzy time-series analysis. This article presents an improved method of forecasting based on L–R fuzzy sets as membership functions. As an illustration, the methodology is employed for forecasting India's total foodgrain production. For the data under consideration, superiority of proposed method over other competing methods is demonstrated in respect of modelling and forecasting on the basis of mean square error and average relative error criteria. Finally, out-of-sample forecasts are also obtained.

Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis

Network traffic prediction plays a fundamental role in characterizing the network performance and it is of significant interests in many network applications, such as admission control or network management. Therefore, The main idea behind this work, is the development of a WIMAX Traffic Forecasting System for predicting traffic time series based on the daily and monthly traffic data recorded (TRD) with association of feed forward multi-layer perceptron (FFMLP). The quality of forecasting WIMAX Traffic obtained by comparing different configurations of the FFMLP that consist of investigating different FFMLP model architectures and different Learning Algorithms. The decision of changing the FFMLP architecture is essentially based on prediction results to obtain the FFMLP model for flow traffic prediction model. The different configurations were tested using daily and monthly real traffic data recorded at each of the two base stations (A and B) that belongs to a Libyan WiMAX Network. We evaluate our approach with statistical measurement and a good statistic measure of FMLP indicates the performance of selected neural network configuration. The developed system indicates promising results in which it successfully network traffic prediction through daily and monthly traffic data recorded (TRD) association with artificial neural network.

The Relationships of Prior Information and Interval Partition on the Forecasting Effect of Fuzzy Time Series Two-Factor Model

This study uses two data sets, Taiwanese export values as the prediction varialbe and its foreign exchange spot rates as the auxiliary variable, to discuss two forecasting issues in the fuzzy time series analysis by using One-and Two-factor models. First, disregarding the length of information period, when window basis is two, a better forecasting result is obtained. Second, the optimum number of partition equal intervals is to select 14 intervals to have the smallest MSE in all models. However, if partitioning the information into more than 14 equal intervals, MSE can not be reduced but presents a waving pattern.

Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform

Fuzzy time series analysis has been used successfully for forecasting in various domains including stock performance, academic enrollment, temperature, and traffic patterns. Research in this field has concentrated primarily on two issues: the reasonable partition of discourse, and defuzzification methods for discrete datasets. Both issues have a huge impact on the prediction performance of forecasting models. This paper integrates the entropy discretization technique with a Fast Fourier Transform (FFT) algorithm to develop a novel fuzzy time series forecasting model to resolve these issues. The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow-Jones Industrial Average (DJIA) financial datasets were used to evaluate the model's performance. The results demonstrate that the presented model is a major improvement over previous fuzzy time series models produced by Chen (1996), Yu (2005), Chang et al. (2011), and Hsieh et al. (2011), and five other conventional time series models. The proposed model is implemented using the bootstrapping method, after which it incrementally updates its prediction capability. Results show that the proposed model's incremental learning mechanism allows it to effectively handle large online financial datasets.

Fault Prognosis Approach for Satellite Attitude Control System Based on T-S Model and Time Series Analysis

A new method based on T-S fuzzy model and time series analysis is proposed for predicting faults in satellite attitude control system. Firstly, satellite attitude control system with nonlinearity and uncertainty is modeled. The residual errors can be obtained by fuzzy parity equation and they are only sensitive to the output of specific actuators (or sensors). Secondly, the time series of the output errors are used to build the autoregressive (AR) model. Therefore, the faults in the satellite attitude control system are predicted by using the AR model. Finally, the results of fault prognosis are given by fault probability. The confidence factor is determined which shows the confidence level of the fault prognosis.

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.

A new approach based on the optimization of the length of intervals in fuzzy time series

In fuzzy time series analysis, the determination of the interval length is an important issue. In many researches recently done, the length of intervals has been intuitively determined. In order to efficiently determine the length of intervals, two approaches which are based on the average and the distribution have been proposed by Huarng [4]. In this paper, we propose a new method based on the use of a single variable constrained optimization to determine the length of interval. In order to determine optimum length of interval for the best forecasting accuracy, we used a MATLAB function which is employing an algorithm based on golden section search and parabolic interpolation. Mean square error is used as a measure of forecasting accuracy so the objective function value is mean square error value for forecasted observations. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results.

Fuzzy Hypothesis Testing and Time Series Analysis of Rolling Bearing Quality

Poor information means incomplete and insufficient information, such as unknown probability distributions and trends. Evaluation for the evolvement of the rolling bearing quality as a time series belongs to the category of information poor process. Statistics relied on known probability distributions and trends could become ineffective. For this end, a fuzzy hypothesis testing model is proposed to make variability analysis of a time series with poor information. By introducing the weight into the rejection region, the relationship of the improved equivalence relation and the empirical confidence level is established, laying the new foundation for a fuzzy decision-making for a time series with poor information. The model is characterized by permitting the probability distribution and the trend of a stationary or nonstationary time-series to be unknown. The experimental investigation on the friction torque of a rolling bearing shows that the model is correct and effective.

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

質的データのファジィ時系列分析

質的データのファジィ時系列分析