Fuzzy Time Series Research Articles

Sales Prediction of Palu Arshop Clothing Using the High Order Chen Fuzzy Time Series Method

Introduction: Arshop is one of the clothing stores in Palu City that is in great demand by the community. As one of the many clothing stores in Palu City Arshop to find a strategy to increase sales. One way that can be used is to make predictions to determine strategies to increase sales. Method: Higher-order Chen fuzzy time series method to predict the time series data of Arshop Palu clothing sales. Chen's high-order fuzzy time series is a time series analysis that can capture varied data patterns, one of which is seasonal patterns, and is formed based on two or more data in the past. Results and Discussion: The results of this study indicate that the high-order Chen fuzzy time series method has an accuracy rate of MAPE 15.59%, which is categorized as good the prediction results of the comparison between various orders show that the fourth-order Chen fuzzy time series is the best for predicting clothing sales of Arshop Palu. Conclusion: The prediction of clothing sales at Arshop Palu using the higher-order Chen fuzzy time series method resulted in a MAPE of 15.59%, which shows good accuracy because it is less than 20%. Based on the comparison of the accuracy values of the four orders, the fourth-order FTS proved to be the most effective for predicting the clothing sales of Arshop Palu.

Comparison of the Fuzzy Time Series Chen Model and the Heuristic Model in Forecasting the Number of International Tourists in West Sumatra

The Fuzzy Time Series Chen and Heuristic are two forecasting methods based on fuzzy logic used to predict values in time series. The FTS Chen and Heuristic models have almost identical forecasting processes, but the main difference lies in how they develop fuzzy logical relationships. The FTS Chen model uses Fuzzy Logical Relationship Groups obtained from the results of Fuzzy Logical Relationships for the forecasting process. On the other hand, the FTS Heuristic model uses Fuzzy Logical Relationships directly in the forecasting process. Fuzzy Logical Relationships are a collection of fuzzy logical relationships used to connect values in time series. By using Fuzzy Logical Relationships, the Heuristic model can predict values in time series more accurately and effectively. The forecasting is done to plan the development of tourism infrastructure, determine service needs, and optimize tourism promotion. The data shows that the number of foreign tourists visiting West Sumatra has continued to grow from 2006 to 2023. The comparison of the accuracy of the forecasting results of FTS Chen and Heuristic models for foreign tourists in West Sumatra yielded a MAPE of 0.241% for FTS model Chen and 0.194% for FTS model Heuristic. This indicates that the best forecasting model for foreign tourists is the Heuristic model due to its lower MAPE value.

Comparison of Chen's Fuzzy Time Series and Triple Exponential Smoothing in Forecasting Medicine Stocks at the Blang Cut Kuala Community Health Center

Forecasting is estimating future conditions by examining conditions in the past. In social life, everything is uncertain and difficult to predict precisely, so forecasting is needed. Efforts are always made to make forecasts in order to minimize the influence of this uncertainty on a problem. In other words, forecasting aims to obtain forecasts that can minimize forecast errors, which are usually measured by the mean absolute percentage error. This method is usually used for time series-based forecasting and uses data or information from the past as a reference when predicting current data. This research will compare the application of the Fuzzy Time Series Chen method and the Triple Exponential Smoothing method in forecasting drug stock determination at the Kuala Community Health Center, Blang Mangat District, Lhokseumawe City Regency, Aceh. The research results showed that the Triple Exponential Smoothing method was better in forecasting drug stock inventories compared to Chen's Fuzzy Time Series method. Chen's Fuzzy Time Series method produces a MAPE value of 17.67%, which means it has an accuracy of 82.33%, while the Triple Exponential Smoothing method produces a MAPE value of 9.842%, which means it has an accuracy of 90.158%

Peramalan Tingkat Penghunian Kamar Hotel Bintang Lima Provinsi Bali Menggunakan Metode ARIMA dan Fuzzy Time Series Lee

This research focuses on forecasting the Occupancy Rate of five-star hotels in the Province of Bali. The increase in Occupancy Rate directly impacts the increase in the number of hotel rooms, which is a key factor for Bali Province as a leading tourist destination in Indonesia and globally. The data used spans from November 2021 to November 2023, with the methods employed being ARIMA with a model of (1,1,1) and Fuzzy Time Series Lee. After conducting the research, the accuracy of the forecast is evaluated through Mean Absolute Percentage Error (MAPE). The MAPE for ARIMA (1,1,1) is found to be 71.4413, while the Fuzzy Time Series Lee method yields a MAPE of 1.1778. With a significantly lower MAPE, the Fuzzy Time Series Lee method is considered more accurate in forecasting the Occupancy Rate of five-star hotels in the Province of Bali for the December 2023 period, with a forecast result of 70.4033%.

A comparative study of simulink fuzzy inference system time series method and traditional time series methods for forecasting the air quality index

A comparative study of simulink fuzzy inference system time series method and traditional time series methods for forecasting the air quality index

PREDIKSI NILAI PENGADAAN BARANG DAN JASA PADA SEBUAH PERUSAHAAN PARIWISATA MENGGUNAKAN METODE ARIMA DAN FUZZY TIME SERIES

PT XYZ does not have a tested evaluation metric to measure the accuracy of procurement prediction models. This research aims to find a suitable metric by comparing two methods: Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS). Both methods were chosen based on the ability of ARIMA to handle time patterns and trends, and the flexibility of FTS in dealing with uncertainty in procurement values. The research uses Root Mean Squared Error (RMSE) values to measure prediction accuracy. The ARIMA implementation analyzes historical data to forecast patterns, while FTS accommodates fluctuations and uncertainties, allowing for more adaptive and accurate predictions. The analysis results show that the ARIMA model has an AIC value of 3953.57 and a residual value of 3351745.26, while FTS has a residual of -224.79. The RMSE evaluation shows the ARIMA value of 3351745.30 and FTS of 224793895.00. The predicted value of ARIMA is 440,326,255, while FTS is 668,471,895. Based on these results, FTS shows superior prediction accuracy compared to ARIMA. FTS is recommended to be implemented at PT XYZ due to its ability to effectively manage uncertainty and fluctuations in procurement values, enabling more accurate strategic decisions. Further research is needed to understand the factors that influence the performance difference between these two methods.

Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis

This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To support this estimation, a dataset consisting of NSINAR(1) realizations with a sample size of n = 1000 is created. These input values are then subjected to fuzzification via fuzzy logic. The prowess of artificial neural networks in pinpointing fuzzy relationships is harnessed to improve prediction accuracy by generating output values. The study meticulously analyzes the enhancement in smoothing of spectral function estimators for NSINAR(1) by utilizing both input and output values. The effectiveness of the output value estimates is evaluated by comparing them to input value estimates using a mean-squared error (MSE) analysis, which shows how much better the output value estimates perform.

A novel optimization model based on fuzzy time series for short-term Air Quality Index forecasting

The problem of air quality forecasting has caused a heated discussion among scholars at home and abroad. Short term air quality forecasting substantially impacts regulatory effectiveness to improve environmental and human health. Therefore, many scholars have proposed a lot of Air Quality Index (AQI) forecasting models to improve air quality forecasting performance. However, the fuzziness of air quality data is often ignored, which may reduce the effectiveness of the forecasting. In this study, a new fuzzy forecasting system that includes a fuzzy time series, data preprocessing technique, multi-objective bat optimization algorithm, and forecasting algorithms is proposed to increase air quality forecasting performance and accuracy. To evaluate the forecasting system effectiveness, one-hour AQI data collected from four cities in China are applied in three experiments. The experiment results find that the proposed forecasting system can effectively utilize the fuzziness of air quality data, and it not only improves the forecasting accuracy but also achieves a higher degree of certainty, which can effectively assist air quality supervision.

A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting

AbstractThe endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The “Wilcoxon Signed‐Rank test” and “Friedman and Nemenyi hypothesis test” are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper.

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

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

Intuitionistic fuzzy time series forecasting method based on dendrite neuron model and exponential smoothing

Methods based on artificial neural networks for intuitionistic fuzzy time series forecasting can produce successful forecasting results. In the literature, exponential smoothing methods are hybridised with artificial neural networks due to their simple and efficient structures to improve the forecasting performance. The contribution of this paper is to propose a new forecasting approach combining exponential smoothing methods and intuitionistic fuzzy time series. In this study, a forecasting algorithm based on the dendrite neuron model and simple exponential smoothing methods is proposed for modelling intuitionistic fuzzy time series. In the fuzzification stage of the proposed method, the intuitionistic fuzzy c-means method is used. The proposed method is a modular method using two separate dendrite neuron model neural networks and the grey wolf optimisation algorithm is used to estimate all parameters of the method. The performance of the proposed method is applied on four different random time series obtained for Index of Coin Market Cap and the performance of the method is compared with some other fuzzy forecasting methods. As a result of the analyses, it is concluded that the proposed modular method has better forecasting results than other methods.

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

Long-term prediction of time series based on fuzzy time series and information granulation

Long-term prediction of time series based on fuzzy time series and information granulation

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%.

Modeling of limit order book data with ordered fuzzy numbers

This paper presents a novel approach to representing the Limit Order Book data at a given timestamp using the Ordered Fuzzy Numbers concept. The limit order book contains all buy and sell orders placed by investors, updated in real-time, for the most liquid securities, even several hundred times a minute. Due to its irregular nature (different and dynamic changes in the number of buy and sell orders), direct calculations on the order book data are not feasible without transforming it into feature vectors. Currently, most studies use a price level-based data representation scheme when applying deep learning models on limit order book data. However, this scheme has limitations, particularly its sensitivity to subtle perturbations that can negatively impact model performance. On the other hand, the ordered fuzzy number is a mathematical object (a pair of two functions) used to process imprecise and uncertain data. Ordered Fuzzy Numbers possess well-defined arithmetic properties. Converting the limit order book data to ordered fuzzy numbers allows the creation of a time series of ordered fuzzy numbers (order books) and use them for further calculations, e.g., to represent input data for deep learning models or employing the concept of fuzzy time series in various domains, such as defining liquidity measures based on limit order book data. In this paper, the proposed approach is tested using one-year market data from the Polish Stock Exchange for the five biggest companies. The DeepLOB model is employed to predict mid-price movement using different input data representations. The proposed representation of Limit Order Book data demonstrated remarkably stable out-of-sample prediction accuracy, even when subjected to data perturbation.

COVID-19 Case Growth Prediction Using a Hybrid Fuzzy Time Series Forecasting Model and a Machine Learning Approach

The COVID-19 pandemic has evolved into a global health crisis, with Indonesia particularly affected due to its high death rates compared to the rest of Asia. A significant number of unacknowledged, undocumented, or unaddressed cases further exacerbate the situation in Indonesia. Challenges arise from the growing patient population and a scarcity of resources, medical experts, and facilities. This study analyzes the daily development of COVID-19 cases in Indonesia, aiming to estimate the number of confirmed cases, recoveries, and fatalities. Introducing a novel hybrid forecasting model, we utilize the Holt-Winter triple exponential smoothing statistical method and the fuzzy time series rate of change algorithm. We apply the Triple Exponential Smoothing Holt Winter statistical model to predict future periods to the fuzzy time series. Based on the testing results, our proposed hybrid forecasting model demonstrates a very high level of predictive capacity. The acquired data are highly accurate, with a 0.15 percent confirmation rate, 0.15 percent recovery rate, and a 0.20 percent mortality rate, along with an average absolute error of less than 10% for each COVID-19 case. The findings indicate that early awareness by the COVID-19 Task Force of the status of cases is highly advantageous. This awareness can aid in formulating appropriate policies for future planning, organization, and accelerated treatment of COVID-19 in Indonesia. Consequently, successful efforts can be made to slow the emergence and spread of COVID-19 in the country.

Singh's Fuzzy Time Series Forecasting Modification Based on Interval Ratio

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

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.

Fuzzy time series dalam meramalkan jumlah produksi karet di Sumatra Utara

Rubber (Hevea brasiliensis) belongs to the genus Heveadari familia Euphorbiaceae which is a tropical woody tree native to the amazon jungle. Rubber is one of the plantation crops that is very important for the economy in Indonesia. Rubber production in North Sumatra has increased every year. To find out whether the amount of rubber production in North Sumatra increases or decreases next year by forecasting the amount of rubber production and getting better forecasting results in the future. This estimate can use the fuzzy time series forecasting method. The fuzzy time series uses the fuzzy set theory as the basis for calculations and a concept used to forecast actual data formed with linguistic variables. The method used in predicting the amount of rubber production in North Sumatra is the fuzzy time series method with data from 1997 to 2021. And the result of forecasting rubber production in 2022 is 261997 with MAPE of 0.54%.
 Keywords: Forecasting, rubber production, fuzzy time seriesMSC2020: 62M10, 62M20, 62M86, 03E72