Fuzzy Time Series Method Research Articles

Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods.

Lumpy skin disease (LSD) is an important transboundary disease affecting cattle in numerous countries in various continents. In Thailand, LSD is regarded as a serious threat to the cattle industry. Disease forecasting can assist authorities in formulating prevention and control policies. Therefore, the objective of this study was to compare the performance of time series models in forecasting a potential LSD epidemic in Thailand using nationwide data. For the forecasting of daily new cases, fuzzy time series (FTS), neural network auto-regressive (NNAR), and auto-regressive integrated moving average (ARIMA) models were applied to various datasets representing the different stages of the epidemic. Non-overlapping sliding and expanding window approaches were also employed to train the forecasting models. The results showed that the FTS outperformed other models in five of the seven validation datasets based on various error metrics. The predictive performance of the NNAR and ARIMA models was comparable, with NNAR outperforming ARIMA in some datasets and vice versa. Furthermore, the performance of models built from sliding and expanding window techniques was different. This is the first study to compare the forecasting abilities of the FTS, NNAR, and ARIMA models across multiple phases of the LSD epidemic. Livestock authorities and decision-makers may incorporate the forecasting techniques demonstrated herein into the LSD surveillance system to enhance its functionality and utility.

Application of Fuzzy Time Series Method Cheng Model in Forecasting Stock Prices PT Bukit Asam Tbk

Application of Fuzzy Time Series Method Cheng Model in Forecasting Stock Prices PT Bukit Asam Tbk

Implementasi Metode Fuzzy Time Series Untuk Meramalkan Jumlah Ekspor Produk Kopi Dari Indonesia

The current trade era opens up opportunities for trading Indonesian coffee in the international market. The number of future exports can be predicted by knowing past export prices. The method that can be used to predict the number of Indonesian coffee exports to various countries is the Fuzzy Time Series method. The Fuzzy Time Series method, the forecasting technique, utilizes fuzzy sets as the basis for the prediction process. In this study, the forecasting stages include defining Xmax and Xmin, determining how many and how long class intervals are, defuzzification and defining fuzzy sets, fuzzifying data on the number of coffee exports, determining fuzzy logic relationships (FLR), establishing fuzzy logical groups (FLRG) relationships, defuzzification, and perform forecasting calculations. Using the Fuzzy Time Series Method, the results of forecasting Indonesia's coffee exports for the following year are 376371.3 tons of coffee products to be exported so that exporters can see this opportunity Keywords: Coffee, export, forecasting, fuzzy time series

Applied of Analytical Hierarchy Process and Fuzzy Time Series in Hybrid for Optimizing Smart Vertical Farming with Multi-Variety Plants

Vertical Farming is a kind of modern agricultural methods, where the structure of growing racks are arranged upwards. This method aims to optimize the use of agricultural space. There are many plants, which are suitable to be planted for vertical farming, such as Strawberry, Tomatoes, Celery, Chili, Mint, Chives, Kuchay, Spinach, and Water spinach. The problem, which is studied in this paper, is how to control the environments of vertical farming with multi-variety plants. This paper proposed a hybrid method of Analytical Hierarchy Process and Fuzzy Time Series AHP-FTS, that is, plants with similar characteristics are placed at the same block area determined by the method of Analytical Hierarchy Process (AHP). Furthermore, controlling the environments regarding the needs of appropriate growing parameters for multi-variety plants, the Fuzzy Time Series (FTS) method is used. Then, time variable for activating actuators could be adjusted as a multi-control system. The effectiveness of the proposed method was evaluated with 365 record data in 12 months. The result shows that the AHP was successful to determine the multi-criteria to determine the zone and priority of plants. The second stage is that the FTS predicts the temperature to determine time variable for activating actuators, and the third stage is the implemented AHP-FTS as a hybrid system to evaluate the vertical Farming system. The results show that the proposed system works well as hybrid system of AHP-FTS

Renewable energy generation forecasting in Turkey via intuitionistic fuzzy time series approach

Recently, renewable energy generation (REG) forecasting has become a challenging task for effective energy management. To date, a number of time-series models utilizing physical models, statistical techniques, and artificial intelligence algorithms have been proposed to deal with this problem. Fuzzy time series (FTS) models stand out among these methods since they do not need extensive historical data or some statistical assumptions like the normal distribution. Because intuitionistic fuzzy sets incorporate non-membership values in addition to membership values, they provide more information than conventional fuzzy sets do. In this regard, this paper recommends a hybrid methodology that combines the intuitionistic FTS method with the intuitionistic fuzzy c-means method for REG forecasting of Turkey between the years 2000–2020. According to the empirical results, it can be concluded that the presented integrated model showed good performance in terms of accuracy. The suggested framework is a useful tool not only for REG forecasting but also for other time series forecasting problems.

Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit

Oil palm plays a crucial role in agriculture and plantations in Indonesia as a commodity with high economic potential. Net Fresh Fruit Bunches (FFB) production is an essential desired outcome in an oil palm plantation. Net FFB is utilized as the primary raw material for the production of Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). The existing challenge is that companies seek to achieve precise quantities and timing for net FFB production in oil palm. One proactive measure to address this is by predicting the net FFB production. Therefore, the objective of this research is to forecast net FFB production by comparing triple exponential smoothing and fuzzy time series methods. Data processing results demonstrate that both forecasting methods yield excellent quality predictions for net FFB production. In the conducted testing, both methods achieved low forecast error values, with MAPE of 11.14670196% and 10.44596891% respectively. However, fuzzy time series exhibited a lower error value compared to the triple exponential smoothing method. Based on these findings, it can be concluded that fuzzy time series is the most reliable model for accurately predicting net FFB production. The advantage of fuzzy time series in forecasting net FFB production can provide significant benefits for companies in determining appropriate strategies for future planning.

PENERAPAN METODE FUZZY TIME SERIES MENGGUNAKAN PARTICLE SWARM OPTIMIZATION ALGORITHM UNTUK PERAMALAN INDEKS SAHAM LQ45

Stocks have a volatile nature and it is difficult to predict the ups and downs. Therefore, stock data forecasting is done by investors to get a picture of future results. Fuzzy Time Series is a time series method that is suitable for forecasting fluctuating stock data because it does not require the fulfillment of assumptions such as normality and stationarity, but the Fuzzy Time Series method has weaknesses in determining intervals. So that in this study, interval optimization will be carried out on Fuzzy Time Series with Particle Swarm Optimization algorithm to predict LQ45 stock index data, Particle Swarm Optimization algorithm is used because it produces more optimal interval values compared to other optimization methods such as Genetic Algorithm. The data to be used is the closing price of the LQ45 stock index on January 5, 2020 to December 26, 2021. Forecasting using the Fuzzy Time Series method produces a SMAPE value of 1.53%, then after optimization using the Particle Swarm Optimization algorithm, the SMAPE value decreases to 1, 27%. Therefore, it can be concluded that optimization using Particle Swarm Optimization on Fuzzy Time Series produces a more optimal forecasting value.

Computational-based partitioning and Strong [formula omitted]-cut based novel method for intuitionistic fuzzy time series forecasting

In this article, we propose a computational-based partitioning (CBP) and Strong α,β-cut based novel intuitionistic fuzzy time series (IFTS) forecasting method. Construction of intervals, intuitionistic fuzzification of time series data, appropriate intuitionistic fuzzy logical relationships (IFLRs), and procedure of defuzzification are critical issues that affect the forecasting accuracy of any IFTS method. A Computational-based partitioning approach uses basic statistical parameters to determine the number of intervals and constructing of intervals without specialized knowledge of the domain. For intuitionistic fuzzification of time series data, all those intuitionistic fuzzy sets are taken having both non-zero membership and non-membership grade. A Strong α,β-cut are used to choose apposite IFLRs that deliver importance in investigating the tendency of time series data. We also propose a defuzzification approach to get the numerical values. In this article, two popular historical time series datasets are used to demonstrate the supremacy of the proposed forecasting method. Root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to verify the performance of the proposed method. Verification of the validity of the proposed forecasting method is authenticated by using the values of the Theil inequality coefficient (U) and tracking signal (TS).

The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality

There is a lot of indoor air pollution, especially from cigarette smoke, wall paint, air fresheners and gas. With this situation, the room uses Air Box WP6003 air quality detection device by transmitting information about air quality through visualization index. This study aims to improve prediction accuracy with fuzzy time series methods processed through 2 naïve and moving average models using forecast transformers and without transformers. The level of prediction accuracy is calculated through several metrics, namely Mean Absolute Percentage Error (MAPE), Sum of Squares Error (SSE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). These results can be calculated between the actual value and the predicted value. The data used is 204584 data from 4 parameters including Temperature, TVOC, HCHO and CO2. The test results with the difference from the forecast transformer and without transformer are comparable. Temperature value obtained using naïve with transformer from RMSE of 0.158866 and naïve without transformer of 0.782397, data using moving average with transformer obtained by 0.147546 and moving average without transformer of 0.772570. This can be explained by the error analysis that was tried, where the error rate continued to increase so that the experimental results continued to be far from the actual number. From the test results it can be concluded that the accuracy of air quality prediction using naïve forecast transformer is pretty accurate.

PENERAPAN METODE FUZZY TIME SERIES-MARKOV CHAIN DALAM PERAMALAN CURAH HUJAN SEBAGAI JADWAL TANAMAN PADI

Rice plants are plants that depend on rainfall because it affects the productivity and quality of rice plants. The purpose of this study is to forecast rainfall in order to determine the right time to plant rice. This study uses the Fuzzy Time Series-Markov Chain method which is a hybrid Fuzzy Time Series method with a markov chain stochastic process. This study uses monthly rainfall data in Kab. Mandailing Christmas from January 2017- December 2022 in mm (millimeter). The data was taken online from the official BPS-North Sumatra website. The results showed that the Fuzzy Time Series-Markov Chain method has a MAPE value of 4.30% which means it has a very good forecasting accuracy value because it is less than 10%. So it can be concluded that forecasting with FTS-MC is a fairly good method in predicting rainfall so that it can forecast for the following month.

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.

Comparison of Forecasting Using Fuzzy Time Series Chen Model and Lee Model to Closing Price of Composite Stock Price Index

Investment is an activity to invest with the hope that someday you will get a number of benefits from theinvestment result. In investing, analyzing is important to see the current situation and condition of stock. Investorscan forecast stock prices by looking at trends based on data movements from stock prices in the past. Fuzzy TimeSeries (FTS) was used in this study to forecast. Fuzzy time series is a forecasting technique that uses patterns frompast data to project future data in areas where linguistic values are formed in the data. This study compares theclosing price of composite stock forecasting using the fuzzy time series chen and lee models. The JCI's closing pricefor the following period is 6,904 and has a Mean Absolute Percentage Error (MAPE) of 4.03%, according to the chenfuzzy time series method. In contrast, utilizing Lee's fuzzy time series method, the predicted JCI closing price for thefollowing period is 7,046, with a MAPE value of 3.10 percent. It can be concluded from the forecasting results of theChen and Lee methods that the Lee model FTS is superior to the Chen model FTS in predicting the JCI closing price.

Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications

High-dimensional time series increasingly arise in the Internet of Energy (IoE), given the use of multi-sensor environments and the two way communication between energy consumers and the smart grid. Therefore, methods that are capable of computing high-dimensional time series are of great value in smart building and IoE applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, the existing FTS models can be unfeasible if all variables were used to train the model. We present a new methodology named Embedding Fuzzy Time Series (EFTS), by applying a combination of data embedding transformation and FTS methods. The EFTS is an explainable and data-driven approach, which is flexible and adaptable for many smart building and IoE applications. The experimental results with three public datasets show that our methodology outperforms several machine learning based forecasting methods (LSTM, GRU, TCN, RNN, MLP and GBM), and demonstrates the accuracy and parsimony of the EFTS in comparison to the baseline methods and the results previously published in the literature, showing an enhancement greater than 80%. Therefore, EFTS has a great value in high-dimensional time series forecasting in IoE applications.

Forecasting Shallot Prices in West Sumatra Province Using the Fuzzy Time Series Method of the Singh Model and the Cheng Model

Shallots are one of the leading spices that are widely used by humans as food seasoning and traditional medicine. The price of shallots always fluctuates which can affect the buying and selling of consumers and producers. Therefore, forecasting is used as a reference to be able to predict the price of shallots in the future and can provide convenience to the public for the condition of shallot prices in the next period. The forecasting method used is the fuzzy time series (FTS) method. FTS is a method whose forecasting uses data in the form of fuzzy sets sourced from real numbers to the universe set on actual data. Forecasting models used in this study are Singh's FTS model and Cheng's model. The data used is monthly data on shallot prices in West Sumatra Province for the period January 2018 to March 2022. The results obtained in this forecast are the Singh model FTS has a smaller MAPE value of 4.41% with a forecasting accuracy value of 95.59 %. This means that Singh's FTS model is better at predicting the price of shallots in West Sumatra Province.

A Novel Fuzzy Time Series Method Based on Dynamic Ridge Polynomial Neural Network With Penalty Term and Fuzzy Clustering Analysis

A Novel Fuzzy Time Series Method Based on Dynamic Ridge Polynomial Neural Network With Penalty Term and Fuzzy Clustering Analysis

Forecasting stock prices based on multivariable fuzzy time series

<abstract><p>With the development of the stock market, the proportion of the stock assets in the asset structure of the residents increases rapidly. Therefore, the research on the prediction of stocks has great theoretical significance and application potential. A key point of researching stock prices is how to pick out the main factors. In this study, principal component analysis (PCA) is applied to find out the main factors which mainly affect the stock price. Then an improved cluster analysis algorithm is proposed to fuzzy the data, and a qualitative analysis method is given to find the most suitable prediction set from the multiple fuzzy sets corresponding to the current fuzzy set. We also extend the inverse fuzzy number formula to a more general form to get the predicted value. Finally, Xishan Coal and Electricity Power (XSCE) and Taiwan Futures Exchange (TAIFEX) time series are predicted, using the proposed multivariate fuzzy time series method. The results show that the prediction error is lower than that of the previous models. The proposed method produces better forecasting performance.</p></abstract>

Peramalan Harga Beras dengan Metode Double Exponential Smoothing dan Fuzzy Time Series (Study Kasus : Harga Beras di Kota Mataram)

Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 566312.340 and based on the MAPE value of 6.75%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method. Keywords: Double Exponential Smoothing Holt, Fuzzy Time Series Cheng, Rice Price, MAPE, MSE

A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges

Abstract: Time series forecasting is a powerful tool in planning and decision making, from traditional statistical models to soft computing and artificial intelligence approaches several methods have been developed to generate increasingly accurate forecasts. Fuzzy Time Series (FTS) methods have been introduced in the early 1990’s to handle data uncertainty and to undercome the statistical assumptions of linearity. Many studies have been reporting their good accuracy, simplicity, potential for interpretability and reduced computational complexity. This paper presents a tutorial for FTS methods. First, a review of the relevant literature is made, offering a foundation on the main concepts and FTS-based models for different time series and different types of forecasts. Then, the current challenges and possible solutions, are discussed alongside a timeline of the research developed in this area by the authors that aims at filling some of these gaps. Finally, a tutorial on the pyFTS library is presented. PyFTS is an open and free library coded in Python programming language that was developed by the MINDS Lab (Laboratory of Machine Intelligence and Data Science) and, also provides a set of transformation functions for pre-processing time series and a set of metrics and databases for benchmarking, in addition to implementing several FTS models in the literature.

An embedding-based non-stationary fuzzy time series method for multiple output high-dimensional multivariate time series forecasting in IoT applications

An embedding-based non-stationary fuzzy time series method for multiple output high-dimensional multivariate time series forecasting in IoT applications

TIME SERIES MODELING OF NATURAL GAS FUTURE PRICE WITH FUZZY TIME SERIES CHEN, LEE AND TSAUR

Investment is the process of investing money or capital for profit or material results. The investor carefully calculates the investment object to minimize losses and maximize profits. One of the essential investment objects is the futures price of natural gas considered a commodity that plays a vital role in the Indonesian economy. The movement of natural gas futures prices can be modeled using a time series model. The data in the time series model is believed to have particular pattern to model the data in the future. The natural gas futures price is modeled into a time series method by using fuzzy time series (FTS) approach of the FTS Chen, Lee and Tsaur. Model accuracy is calculated using the criteria of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The three FTS methods have good performance of accuracy for this time series data, where FTS Tsaur as fuzzy times series approach with average based method shows the best results with the smallest error rate to the data of natural gas future price.