Fuzzy Time Series Model Research Articles

Metamorphosing forex: advancements in volatility forecasting using a modified fuzzy time series framework

The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall.

Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy time series models offer a promising solution for forecasting such data, but a key challenge lies in partitioning the universe of discourse, which significantly impacts forecasting accuracy. Traditional fuzzy time series models often use equal-length interval partitioning, which is more suited for stationary data and limits their adaptability to non-stationary time series. This paper introduces a novel variable-length interval partitioning method designed specifically for non-stationary time series. The developed method combines a Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven partitioning that adapts to the changing characteristics of the data. The LSTM Autoencoder encodes the time series, which is clustered using K-means, and intervals are defined based on cluster centers. Furthermore, the Variable Length Interval Partitioning-based Fuzzy Time Series model (VLIFTS) is developed by incorporating this partitioning method and the concepts of Markov chain and transition probability matrix. In this model, fuzzy sets are viewed as states of a Markov chain, and transition probabilities are used in the forecasting phase. The model is validated on stock market indices Nifty 50, NASDAQ, S&P 500, and Dow Jones. Stationarity and heteroscedasticity are tested using Augmented Dickey-Fuller (ADF) and Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) show that VLIFTS significantly improves forecasting accuracy over traditional models. This hybrid approach enhances fuzzy time series modelling and can be applied to various non-stationary time series forecasting problems.

VWFTS-PSO: a novel method for time series forecasting using variational weighted fuzzy time series and particle swarm optimization

Variational weighted fuzzy time series models are known to perform better than traditional fuzzy time series models, due to their ability to handle outliers and noise. However, the performance of variational weighted fuzzy time series models is highly dependent on the partitioning of the universe of discourse. In this paper, we propose a novel variational weighted fuzzy model integrated with particle swarm optimization for choosing the best possible partitioning of the universe of discourse. Our approach combines the strengths of variational weighted fuzzy time series to handle uncertain and imprecise data and particle swarm optimization techniques to find the best model parameters. We evaluate the performance of the proposed approach on standard time series datasets and compare it with other forecasting methods in the literature. Our findings demonstrate that the proposed approach achieves accurate and reliable forecasts, outperforming other state-of-the-art approaches.

Enhancing operational efficiency of insurance companies: a fuzzy time series approach to loss ratio forecasting in the Egyptian market

ABSTRACT This article analyses the crucial significance of loss ratio (LR) evaluation in assessing the operational efficiency of insurance companies. Fuzzy time series (FTS) models are effective in modelling non-linear with uncertainties and providing adequate performance with limited data availability, which overcomes the shortage of conventional models used in Egyptian insurance market literature. This paper aims to introduce a comprehensive analysis of LR of the property and casualty sectors. Moreover, review and compare various forecasting FTS techniques to discuss the effectiveness of using FTS methods in forecasting LR. The suggested method is based on results from tests done with four models that had Huarng 465 partitions and interval length parts of 5, 10, 50, and 100. The results show LR prediction improved significantly. Yu and Cheng’s models using a Huarng 465 partition for training data and the Yu model with 100 partitions for testing data had high accuracy and low error.

Short-term load forecasting: cascade intuitionistic fuzzy time series—univariate and bivariate models

AbstractShort-term load forecasting (STLF) is essential for developing reliable and sustainable economic and operational strategies for power systems. This study presents a forecasting model combining cascade forward neural network (CFNN) and intuitionistic fuzzy time series (IFTS) models for STLF. The proposed cascading intuitionistic fuzzy time series forecasting model (C-IFTS-FM) offers the advantage of CFNN using the links of both linear and nonlinear to model fuzzy relations between inputs and outputs. Moreover, it offers a more reliable and realistic approach to uncertainty, taking notice of also the degree of hesitation. C-IFTS-FM works in univariate structure when it uses only hourly load data, and in bivariate structure when it uses hourly load data and hourly temperature time series together. The conversion of time series into IFTS is realized with intuitionistic fuzzy c-means (IFCM). Thus, the membership and non-membership values for each data point are produced. In modelling process, membership and non-membership values, in addition to actual lagged observations, are used as input of the CFNNs. The effectiveness of C-IFTS-FM on test sets for both structures was discussed comparatively via different error criteria, in addition, the convergence time was examined, and also the fit of forecasts and observations was presented with different illustrations. Among different combinations of hyperparameters, in the best case, approximately 86% better accuracy is achieved than the best of the others, while even in the case of the worst of hyperparameters combination, the accuracy was improved by over 20% for the PSJM data sets. For HEXING, CHENGNAN, and EUNITE data sets, these progress rates reached approximately 90% in the best case.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease’s transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country’s epidemic pattern and assist the government in developing better effective interventions.

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Minimizing of Forecasting Error in Fuzzy Time Series Model Using Graph-Based Clustering Method

In recent years, numerous fuzzy time series (FTS) forecasting models have been developed to address complex and incomplete problems. However, the accuracy of these models is specific to the problem at hand and varies across datasets. Despite claims of superiority over traditional statistical and single machine learning-based models, achieving improved forecasting accuracy remains a formidable challenge. In FTS models, the lengths of intervals and fuzzy relationship groups are considered crucial factors influencing forecasting accuracy. Hence, this study introduces an FTS forecasting model based on the graph-based clustering technique. The clustering algorithm, utilized during the fuzzification stage, enables the derivation of unequal interval lengths. The proposed model is applied to forecast two numerical datasets: enrollment data from the University of Alabama and the datasets of Gas prices RON95 in Vietnam. Comparisons of forecasting results between the proposed model and others are conducted for enrollment forecasts at the University of Alabama. The findings reveal that the proposed model achieves higher forecasting accuracy across all orders of fuzzy relationships when compared to its counterparts

APPLICATION OF THE FUZZY TIME SERIES CHEN MODEL IN FORECASTING THE RUPIAH EXCHANGE RATE AGAINST THE US DOLLAR (USD)

The importance of the rupiah exchange rate for many individuals in Indonesia in everyday life can be explained by the fact that the country's economy is very dependent on the international economy. Exchange rate used to do future payments using a certain currency and so on the link between two currencies of different countries. In context In international trade, the US Dollar (USD) currency has a very important role very significantly for developing countries because it is used as an eye transaction money. Therefore, the movement of the rupiah exchange rate is an important factor for a country. So forecasting techniques are needed to anticipate exchange rate changes. In this study, the method used is Chen's Fuzzy Time Series (FTS) model to predict the rupiah exchange rate against the United States Dollar (USD) in the future. The results of this study show that forecasting the rupiah exchange rate against the US Dollar (USD) from May 2023 to July 2023 is stable at a value of 1412.40 Rupiah, with a rate average absolute error (MAPE) of 1.6717%.

EARTHQUAKE FREQUENCY DATA MODELING IN MENTAWAI USING FUZZY TIME SERIES LEE AND FUZZY TIME SERIES TSAUR

The Fuzzy Time Series (FTS) was first studied by Song and Chissom based on the theory of fuzzy sets and the concept of linguistic variables and their applications discovered by Zadeh. FTS has several models, namely FTS Lee, FTS Tsaur, and so on. In this study, we will model earthquake frequency data in Mentawai using FTS Lee and FTS Tsaur. The seismicity data used in this study is earthquake frequency data in the Mentawai which are calculated from 1960 to 2022. Additionally, the seismicity data source is taken from the U.S. Geological Survey catalog. Based on MAPE and MSE, the results obtained on the FTS Lee and FTS Tsaur models are MAPE values of 37,511% and 27,051%. And the MSE values obtained were 27,073 and 11,671. Thus, the best model used in modeling data on the frequency of earthquake occurrences in the Mentawai Islands is the Ruey Chyn Tsaur Fuzzy Time Series model.

Implementing lee's model to apply fuzzy time series in forecasting bitcoin price

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.

Implementing lee's model to apply fuzzy time series in forecasting bitcoin price

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.

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.

Implementasi Metode Fuzzy Time Series dalam Peramalan Penjualan Produk Unggulan Perusahaan

This research discusses sales forecasting of two superior products of PT. Pakis Logam Perkasa Indonesia: semi-stainless spatula and electric elbow grater c. One of the household furniture manufacturers in Tulungagung sells its wholesale and retail products. Due to the high demand for semi-stainless spatula and c-elbow electric grater products, the company experienced uncontrolled inbound and outbound sales. Therefore, sales forecasting is imperative. Sales forecasting uses previous data to decide how many products to market. Chen's Fuzzy Time Series model uses a technique for forecasting the demand for semi-stainless spatulas and c-elbow electric grates for the next month based on previous data patterns. The two methods used to analyze data are qualitative analysis and quantitative analysis. The aim of creating this forecasting model is to improve forecasting results, especially in terms of accuracy. Next, the Mean Absolute Percentage Error (MAPE) determines the error value from the forecasting results. The error value for the semi-stainless spatula product is 1.03%, and the c-elbow electric grater is 0.62%. The MAPE test shows that the forecasting results of these two products are excellent.

Analyzing The Inflow And Outflow Of Water In Mettur Dam Using Fuzzy Time Series

Farming is said to play a leading role in present day situation. The crucial part for forming is water. So, the water for farming in the Cauvery delta areas is routinely got from the mettur dam. Here, we are studying water inflow and outflow by Fuzzy time series, Fuzzy time series was acquainted in nearly 1990’s to handle uncertainty data. Fuzzy time series model cannot deal with incomplete data. So here we present the complete inflow and outflow of water in mettur dam for continuous years and develop the algorithm using fuzzy time series and also compute the graphical representation of water inflow and outflow .Using proposed algorithm we also predict the inflow and inflow of water for the future.

A Phase-cum-Time Variant Fuzzy Time Series Model for Forecasting Non-stationary Time Series and its Application to the Stock Market

A Phase-cum-Time Variant Fuzzy Time Series Model for Forecasting Non-stationary Time Series and its Application to the Stock Market

Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy.

Volatility forecasting: a new GARCH-type model for fuzzy sets-valued time series

Volatility forecasting: a new GARCH-type model for fuzzy sets-valued time series

Implementasi Analisis Fuzzy Time Series Dalam Melihat Hasil Komoditas Perikanan Di Provinsi Aceh

Fisheries is one of the sources of food security in Indonesia which plays an important role in the rich biodiversity of Indonesian coastal waters. This research uses the Chen Fuzzy Time Series Model forecasting method. The samples used in this study are data on the number of fishery commodity products with specific Tilapia and Catfish monthly Aceh Province as much as 24 data, starting from January 2021 to December 2022. The results of forecasting the number of tilapia in January 2023 were 1,320,815, with a MAPE (Mean Absolute Percentage Error) value of 12.93%. And the result of forecasting the number of catfish in January 2023 was 2,339,568, with a MAPE (Mean Absolute Percentage Error) value of 22.9%. This shows that forecasting using the Chen model Fuzzy Time Series method is good enough to forecast the number of tilapia and catfish in Aceh Province.

Improving forecasting model for fuzzy time series using the Self-updating clustering and Bi-directional Long Short Term Memory algorithm

This paper proposes a new model for creating fuzzy time series and conducting future forecasts using the Bi-directional Long Short-term Memory model (Bi-LSTM). This study contributes by using both classical machine learning and deep learning techniques. Firstly, a self-updating clustering algorithm is used to determine the optimal number of clusters for time series. Secondly, the study describes a method for determining the fuzzy relationship between elements of time series and the identified clusters. Thirdly, the developed model creates the fuzzy time series based on the established rules. Finally, forecasting for the future value using the Bi-LSTM model with some improvement in optimizing the parameters and increasing the accuracy of forecasting result. The steps of the proposed model are clearly illustrated by a numerical example. Moreover, this model also tested on various datasets, including M3, M4, and M5 datasets. By evaluating its performance using parameters such as sMAPE and RMSSE, the proposed model has shown significant enhancements when compared to existing models. Additionally, the model’s effectiveness is further demonstrated through its successful application in forecasting the VN index stock in Vietnam.