Fuzzy Time Series Model Research Articles

Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

Forecasting with Improved Model of Fuzzy Time Series Based on Hedge Algebras

Enhanced fuzzy time series forecasting model based on hesitant differential fuzzy sets and error learning

Most of existing fuzzy time series forecasting models lead to unsatisfactory forecasting performance due to deficiencies in constructing fuzzy intervals. This paper adopts the Fuzzy Silhouette criterion to determine the optimal number and length of fuzzy intervals effectively and objectively. The universe of discourse with equal and unequal intervals are merged by aggregating hesitant information. Second, multiple relevant factors are considered to reveal the complex attributes of the real-world data, and differential fuzzy sets are included to reconnoiter deep trends. Finally, an optimal error learning mechanism is applied to enhance the forecasting performance. Key strengths of the developed model are verifying the possibility of increasing the performance of conventional fuzzy time series models by integrating them with new components and processes. Three typical stock index datasets are selected to evaluate the performance of the proposed model and experimental results prove that it outperforms compared models in forecasting accuracy and profitable ability.

A Comparison Study on Fuzzy Time Series and Holt-Winter Model in Forecasting Tourist Arrival in Langkawi, Kedah

The tourism industry in Malaysia has been growing significantly over the years. Tourism has been one of the major donors to Malaysia’s economy. Based on the report from the Department of Statistics, a total of domestic visitors in Malaysia were recorded at about 221.3 million in 2018 with an increase of 7.7% alongside a higher record in visitor arrivals and tourism expenditure. This study aims to make a comparison between two methods, which are Fuzzy Time Series and Holt-Winter in forecasting the number of tourist arrival in Langkawi based on the monthly tourist arrival data from January 2015 to December 2019. Both models were generated using Microsoft Excel in obtaining the forecast value. The Mean Square Error (MSE) has been calculated in this study to get the best model by looking at the lowest value. The result found that Holt-Winter has the lowest value that is 713524285 compared to the Fuzzy Time Series with a value of 2625517469. Thus, the Holt-Winter model is the best method and has been used to forecast the tourist arrival for the next 2 years. The forecast value for the years 2020 and 2021 are displayed by month.

WATER CONSUMPTION PREDICTION USING FUZZY TIME SERIES - A CASE STUDY IN PRIVATE COMPANY OF TANGERANG DISTRICT INDONESIA

Consumption of water in the Tangerang Regency continuously increases from year to year due to the increasing population and birth rates an average increase of 3% every year. So, the water demand prediction to be important to meet customer or community needs. The private water utility company needs to use a new method for predicting future monthly water consumption values and improves accuracy when forecasting time series using a visibility graph and presents to make more accurate predictions. In this study, we aim to measure the trend analysis volume of water consumption prediction by Fuzzy Time Series versus actual usage volume. Fuzzy Time Series (FTS) is a concept plan method that uses fuzzy logic that is able to provide predictions (estimates) of time series data analysis for the next several periods. Mean Absolute Percentage Error (MAPE) is obtained for different configurations of the input sets and of the FTS model structure. From the results of the average value error accuracy was only 4.5% using FTS Chen Method and included in the low category and water consumption actual versus prediction with the FTS Chen method shown related stable.

Ordered fuzzy random variables: Definition and the concept of normality

The concept of fuzzy random variable combines two sources of uncertainty: randomness and fuzziness, whereas the model of ordered fuzzy numbers provides a representation of inaccurate quantitative data, and is an alternative to the standard fuzzy numbers model proposed by Zadeh. This paper develops the model of ordered fuzzy numbers by defining the concept of fuzzy random variables for these numbers, called further ordered fuzzy random variables. Thanks to the well-defined arithmetic of ordered fuzzy numbers (existence of neutral and opposite elements) and the introduced ordered fuzzy random variables; it becomes possible to construct fully fuzzy stochastic time series models such as e.g., the autoregressive model or the GARCH model in the form of classical equations, which can be estimated using the least-squares or the maximum likelihood method. Furthermore, the concept of normality of ordered fuzzy random variables and the method to generate pseudo-random ordered fuzzy variables with normal distribution are introduced.

Fuzzy time series forecasting in determining inventory policy for a small medium enterprise (SME) company

<p class="Authors">Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting consumer demand and production volume. It is of greater benefits if we have good forecasting accuracy rates especially in managing inventory in a Small and Medium Enterprise (SME) company. This study focuses on multiple products with single production line. The aims of this study are to propose the appropriate the forecasting method for the products, to develop new inventory policy that minimizes the total inventory cost for the company. Simple forecasting methods like trend line, three month moving average (MA (3) and fuzzy time series forecasting are used in this study. The result shows that fuzzy time series forecasting model is suitable to be used in forecasting future demand for all products. The proposed inventory policy is based on the number of cycle per year and the number of production for each product has helped the company to minimize total inventory cost and schedule the production process accordingly. The proposed inventory policy resulted in lower total inventory cost when compared to current practice.</p><p><strong><em> </em></strong></p>

Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread. In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

RETRACTED ARTICLE: Soft computing-based fuzzy time series model for dynamic vehicle routing problem

Utmost models for vehicle steering detailed in the writing accept consistent travel times. Plainly, disregarding the way that the movement time between two areas does not depend just on the separation voyaged, yet on numerous different variables including time, sways the use of the models to genuine issues. In the present research, a multi-target dynamic vehicle-directing issue with fuzzy time series is displayed. In this issue, majority of the work where information is known ahead of time, some setoff ongoing solicitations arrive arbitrarily after some time and the dispatcher does not have any deterministic or probabilistic data on the area and size of them until they arrive. The manuscript utilizes an immediate understanding of the multi-target dynamic vehicle-directing issue with fuzzy time series as a multi-target issue where the required armada measure, generally all out voyaging separation, and hold-up time forced on vehicles are limited, and the general clients' inclinations for administration are boosted. The presentation of the proposed methodology is assessed in various strides on different test issues summed up from a lot of static occasions in the writing. In the initial step, the exhibition of the proposed methodologies is checked in static conditions and after that, different presumptions and improvements are included progressively, and changes are analyzed. Computational tests on informational collections represent the productivity and adequacy of the proposed methodology.

High-Order Fuzzy Time Series Forecasting by Using Membership Values Along with Data and Support Vector Machine

In the past few years, non-stochastic fuzzy time series (FTS) models have drawn remarkable attention of researchers from different domains. Unlike traditional stochastic models, FTS models do not require any strict assumption on the characteristics of data to be modeled and are applicable to time series even with uncertainty. However, the effectiveness of FTS models largely depends on the determination of effective length of interval and modeling of fuzzy logical relationships (FLRs). Motivated by this, in this paper, we have developed a novel method using fuzzy c-means clustering to determine the unequal-length of interval. Additionally, for the first-time membership values are considered while modeling the FLRs using support vector machine (SVM). The order of the model is determined by analyzing the autocorrelation function and partial autocorrelation function of the time series. To measure the accuracy of the proposed model, ten different time series datasets are considered. Four recently developed fuzzy time series forecasting models and two popular crisp time series forecasting models using MLP and SVM are considered for comparative performance analysis. From the experimental result analysis, it is observed that the proposed model outperforms other alternatives and shows statistically better forecasting accuracy based on the popular Wilcoxon signed-rank test; and Friedman and Nemenyi hypothesis test.

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

Markov Weighted Fuzzy Time-Series Model Based on an Optimum Partition Method for Forecasting Air Pollution

Air pollution is one of the main environmental issues faced by most countries around the world. Forecasting air pollution occurrences is an essential topic in air quality research due to the increase in awareness of its association with public health effects, and its development is vital to managing air quality. However, most previous studies have focused on enhancing accuracy, while very few have addressed uncertainty analysis, which may lead to insufficient results. The fuzzy time-series model is a better option in air pollution forecasting. Nevertheless, it has a limitation caused by utilizing a random partitioning of the universe of discourse. This study proposes a novel Markov weighted fuzzy time-series model based on the optimum partition method. Fitting the optimum partition method has been done based on five different partition methods via two stages. The proposed model is first applied for forecasting air pollution using air pollution index (API) data collected from an air monitoring station located in Klang city, Malaysia. The performance of the proposed model is evaluated based on three statistical criteria, which are the mean absolute percentage error, mean squared error and Theil’s U statistic, using the daily API data. For further validation of the model, it is also implemented for benchmark enrolment data from the University of Alabama. According to the analysis results, the proposed model greatly improved the performance of air pollution index and enrolment prediction accuracy, for which it outperformed several state-of-the-art fuzzy time-series models and classic time-series models. Thus, the proposed model could be a better option for air quality forecasting for managing air pollution.

A new method of time series forecasting using intuitionistic fuzzy set based on average-length

ABSTRACT The major problem in the field of fuzzy time series (FTS) is the accuracy rate in the forecasted values. To overcome this problem here, we propose a model for intuitionistic FTS forecasting based on average-length of interval, which enhances the forecasting result. The proposed model is focused on how to fuzzify the historical time series data. Here, the fuzzification of each observation is intuitionistic fuzzification, which is based on the maximum degree of score function and also establishes intuitionistic fuzzy logical relationships (IFLR) among all intuitionistic fuzzified data set. Here, we use simple arithmetic computations in defuzzification process with measuring the frequency of IFLR. An illustrative example of enrollments at the University of Alabama is used to verify the effectiveness of the proposed model and comparison in terms of RMSE and AFE with some of the existing forecasting models to show its superiority.

Prediction of Indian Monsoon Rainfall by Interval based Simplified High Order Fuzzy Time Series Model

Rain is of uttermost importance for agriculture based economies. Most of the Asian countries, India in particular largely depend on a good rainfall. The prediction of rainfall will not only help government to make better future policies but also farmers and agro based companies can make better future management. Rainfall forecasting involves high degree of uncertainty and for such conditions fuzzy time series and other soft computing techniques are best to deal with. The utility of a forecasting method lies with the accuracy with the predicted values. In this paper rainfall prediction by fuzzy time series model is proposed in which two difference values of the interval corresponding to the fuzzified forecasted value is proposed. This model is tested on real time data of average monsoon rainfall in India. The predicted values are compared with Chen model. The results show that the proposed model have less error compared to Chen’s model.

FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT

Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.

A novel dynamic ensemble air quality index forecasting system

The air quality index (AQI) can reflect the change of air quality in real time. It has linear characteristics, nonlinear and fuzzy features. However, a single model cannot fit the dynamic changes of AQI scientifically and reasonably. Therefore, this paper proposes a new dynamic ensemble forecasting system based on multi-objective intelligent optimization algorithm to forecast AQI, which has time-varying parameter weights and mainly contains three module: data preprocessing module, dynamic integration forecasting module and system evaluation module. In the data preprocessing module, the off-line frequency domain filtering approach is applied to identify and correct the outliers in the series. To better extract the series information and remove the random noise, the time series is decomposed into multi-level utilizing decomposition strategy and reconstructed. In the dynamic integration forecasting module, three hybrid models based on ARIMA, optimized extreme learning machine and fuzzy time series model, named as HCA, HCME and HCFL respectively, are used to forecast the reconstructed series and time varying parameters are employed to dynamically combine the forecasting results. In the system evaluation module, the accuracy of the system was tested by parameter test method and non-parametric test method respectively. The results demonstrate that the proposed dynamic integrated model is not only superior to other comparison models in forecasting accuracy, but also provides strong technical support for air quality forecasting and treatment.

Parsimonious fuzzy time series modelling

This paper proposes a novel modelling structure to ensure the parsimony of fuzzy time series (FTS) models while retaining certain level of out-of-sample accuracy. A parsimonious FTS model requires multiple optimizations of hyper-parameters such as time lags and partitioning which consists of the number of fuzzy sets, the partitioning type and the membership functions. In the vast literature of fuzzy time series, hyper-parameter optimization is usually ignored. In addition to that, optimization process for the hyper-parameters is also not presented properly. In this study, a parsimonious FTS modelling approach is introduced by using genetic algorithm (GA). Three major innovations are proposed: (1) Hyper-parameters of FTS structure are optimized to eliminate subjective preferences with the help of GA. Some of those parameters are never optimized or simply ignored in the past research. (2) The set of hyper-parameters is optimized subject to highest accuracy in validation set data and model’s complexity. (3) For achieving sparsification and accuracy simultaneously at reasonable computation time, a two-stage GA optimization is run to search for higher accuracy and lower complexity consecutively. Empirical studies are conducted on two types of datasets. Prices of liquid bulk cargo carriers (i.e. tanker) and secondhand ship have been predicted using the proposed approach. Potential benchmarks as well as a simple Nave forecast have been compared to the proposed model for validation based on mean absolute scaled error and root mean squared error.

МЕТОДИ ІНТЕЛЕКТУАЛЬНОГО МОДЕЛЮВАННЯ ПРОЦЕСІВ З ПЕРЕМІННИМ ІНТЕРВАЛОМ СПОСТЕРЕЖЕНЬ ТА КОНСТРУКТИВНОГО УПОРЯДКУВАННЯ «З ВАГОЮ»

У статті досліджені актуальні питання щодо моделювання та аналізу недетермінованих процесів, представлених нечіткими часовими послідовностями з нерівномірними інтервалами між спостереженнями. Метою дослідження являється розробка нової сеперабельної моделі та методу аналізу і прогнозування таких часових рядів. Модель відрізняється окремим формуванням послідовностей величин показників та інтервалів між спостереженнями, з подальшим їх узгодженням. Представлено програмних комплекс та результати моделювання, отримані на основі удосконаленої нечіткої квантильної моделі. Запропоновано нові змістовні та формальні постановки завдань щодо упорядкування послідовностей елементів, які відрізняються урахуванням різної складності (ваги) окремих конструктивних операцій. Наводяться інтелектуальні алгоритми реалізації завдань упорядкування «з вагою».

A Fuzzy Time Series Model Based on Improved Fuzzy Function and Cluster Analysis Problem

Based on the improvement in establishing the relations of data, this study proposes a new fuzzy time series model. In this model, the suitable number of fuzzy sets and their specific elements are determined automatically. In addition, using the percentage variations of series between consecutive periods of time, we build the fuzzy function. Incorporating all these improvements, we have a new fuzzy time series model that is better than many existing ones through the well-known data sets. The calculation of the proposed model can be performed conveniently and efficiently by a MATLAB procedure . The proposed model is also used in forecasting for an urgent problem in Vietnam. This application also shows the advantages of the proposed model and illustrates its effectiveness in practical application.

A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.