Fuzzy Time Series Method Research Articles

The Application of Fuzzy Time Series Method for Web-Based Prediction of Household Chemical Product Sales Stock

The seasonal and trend factors of the market demand greatly influence the sales of household chemical products. Therefore, PT. XYZ company requires an accurate sales prediction system to optimize production and inventory. This research aims to develop a web-based sales prediction system for household chemical products using fuzzy time series method. This method considers the factors that influence sales and generates a prediction model that can assist the company in decision-making. This method allows the use of uncertain and unstructured variables in the prediction. The research collected historical sales data of household chemical products from January to December 2023 through interviews, observations, and literature studies. The system was designed using a structured approach and PHP programming language. The Fuzzy Time Series method was used for decision-making. The accuracy of the Fuzzy Time Series method for sales data from January to December 2020 was calculated through program simulation and manual methods, resulting in an accuracy rate of 13.71%. The application provides information on the sales of each product, with the hope that users can reduce the accumulation of stock every month.

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.

Peramalan Cadangan Devisa Menggunakan Metode Double Smoothing Exponential dan Metode Fuzzy Time Series

This study aims to forecast the amount of foreign exchange reserves using two methods, namely Double Smoothing Exponential and Fuzzy Time Series. This foreign exchange reserve data is time series data, namely monthly data starting from January 2017 to February 2024 with a total of 86 monthly data. Furthermore, to see the level of accuracy of the forecast results, the Mean Absolute Percentage Error (MAPE) and MSE values ​​are used where the smaller the error value produced, the more accurate the forecast results from the Double Smoothing Exponential Holt and Fuzzy Time Series Cheng methods. The results of forecasting the amount of foreign exchange reserves in March 2024 using the Holt method are 144434.19 and for the Cheng method are 140008.032. The double exponential smoothing method has an MSE value of 8625715.609 and a MAPE value of 1.69%. Meanwhile, with the fuzzy time series method, the MSE value is 11651319.400 and the MAPE value is 1.96%. Forecasting using these two methods has a very good level of accuracy, because it has a MAPE value below 10%. Based on the results of this analysis, it can be seen that the double exponential smoothing method from Holt has a smaller forecast error size than the Cheng fuzzy time series method. Thus, it can be concluded that the Double Exponential Smoothing method is a better method for predicting the amount of foreign exchange reserves.

Utilization Of Fuzzy Time Series Method Of Analyzing Changes In Rice Prices Throughout The Year In East Java

The research aims to analyze the vertical transmission of rice selling prices from the producer (miller) level to the consumer and analyze what factors influence the formation of rice prices at the producer (miller) level in east java province. The main data used is 2023 data from january to september 2023 with a sample of each rice price in the district/city in east java province, while for the analysis of price formation factors using monthly data on rice prices at the producer (miller) level, rice prices in consumer level, rice consumption, price of imported rice 2. The method used in this research is a descriptive research method with a quantitative approach. The analysis tool used is fuzzy time series. The research results found that the rice price transmission pattern in east java province was symmetrical. In the short term, the rice price transmission pattern is asymmetric, meaning that there is a delay or indirect price transmission mechanism between producers and consumers, and in the long term, this means that consumer prices are transmitted directly to producer prices. (milling). Factors that influence the price of rice at the milling level in east java province are consumer prices, imported rice prices, rice consumption, and climate.

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 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%

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

Forecasting The Number of Health Center Patient Arrivals Using Cheng’s Fuzzy Time Series Method

Forecasting The Number of Health Center Patient Arrivals Using Cheng’s Fuzzy Time Series Method

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

Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain untuk Peramalan Indonesia Crude Oil Price (ICP)

<p>In Indonesia, crude oil plays a significant role in the country’s economy as it serves as a source of income and meets the country's energy needs. Therefore, fluctuations in crude oil prices have a significant impact on the economic activities of the society. Forecasting the price of Indonesian crude oil is thus crucial. The international price of crude oil in Indonesia is known as the Indonesian Crude Oil Price (ICP). One commonly used statistical method for forecasting is the ARIMA method. However, the ARIMA method has certain assumptions that need to be fulfil, and many real-world data cannot meet these assumptions. Hence, forecasting using the Fuzzy Time Series (FTS) method, which does not rely on assumptions, is employed. Some popular FTS methods include the Cheng FTS method and the Markov Chain FTS method. This study implements the Cheng FTS and Markov Chain FTS methods on the ICP data from May 2018 to June 2023 to determine the most appropriate method for forecasting. The analysis results using the Cheng FTS method on the testing data yield a Mean Absolute Percentage Error (MAPE) value of 4,083%, while the Markov Chain FTS method has MAPE value of 4,585%. The Cheng FTS method selected as the appropriate model for forecasting the ICP data since it has a smaller MAPE value. Using the Cheng FTS method, the predicted ICP value for July 2023 is US$72,907 per barrel.</p><p><strong>Keywords</strong><strong>: </strong>ICP; FTS Cheng; FTS Markov Chain; MAPE</p>

A Comparison between ARIMA and Fuzzy Time Series Methods in Predicting Daily COVID-19 Outbreak in Malaysia

A Comparison between ARIMA and Fuzzy Time Series Methods in Predicting Daily COVID-19 Outbreak in Malaysia

FORECASTING INDONESIAN ISLAMIC BANK (BSI) SHARE PRICES USING THE FUZZY TIME SERIES CHENG METHOD

Shares were one of the most popular financial market instruments. In Indonesia, stock market activity continued to increase so that stock investment was in great demand by the public, especially in the banking sector. Indonesia had a majority Muslim population. Based on this, Indonesia had good potential in the field of Islamic finance, especially Islamic banking. One of the Islamic banks that had achieved positive performance was Bank Syariah Indonesia (BSI). BSI's stock price every day from February 1, 2021, to January 11, 2023, tended to experience a downward trend and fluctuated, making it difficult for investors to see the prospects of a company in the future. For this reason, a forecasting technique was needed. A good forecasting method used for data with trend patterns both down and up was Cheng's Fuzzy Time Series (FTS) method. So, this study used Cheng's FTS method to predict BSI's share price in the future. The calculation of the accuracy of the prediction results in this study used Mean Absolute Percentage Error (MAPE). The results showed that the forecasted value of BSI's share price for the period January 12, 2023, to January 31, 2023, was constant at 1,353.267 million with a MAPE value of 3.09%.

Prediksi Tingkat Inflasi Di Indonesia Menggunakan Metode Average Based Fuzzy Time Series

Inflation is a very important indicator in maintaining the stability of the country's economy, so it is necessary to predict the inflation rate to determine the movement of the inflation rate in the future. To predict the inflation rate in Indonesia in 2023, this research uses the Average Based Fuzzy Time Series (FTS) method based on Python programming. This method uses the principle of fuzzy set as the basis of its calculation. The data used in this study are monthly data on inflation rates in Indonesia for the period January 2013 - December 2022 obtained from the official website of Bank Indonesia (BI). The results showed that the prediction of the inflation rate in Indonesia in 2023 was in the range of 5.23% - 5.51% with the highest inflation value occurring in April and the lowest inflation value occurring in January. The accuracy level of the Average Based FTS method is calculated using the Mean Absolute Percentage Error (MAPE) of 0.820849835% which indicates that the method can be used to predict the inflation rate.
 Keywords : Average Based, FTS, Inflation, Prediction, Python

Prediksi Pajak Pertambahan Nilai pada Penyediaan Jasa dengan Metode Fuzzy Time Series Model Chen

For companies, tax is a burden or fee that must be paid to the state as a taxpayer. The taxes that must be paid by the company can affect the profits earned. Therefore, efforts are needed to reduce or minimize the tax burden. Efforts to minimize the tax burden include tax planning. Tax planning that is often used by companies is tax planning on Value Added Tax (VAT), because all production activities are closely related to the VAT burden. Tax planning for VAT can be done by maximizing the amount of input VAT. To be able to identify the amount of input VAT in the next period, predictions can be made on the input VAT value. The uncertain VAT value and limited data collection make it possible to predict the VAT value using the fuzzy time series method. One model that can be used in fuzzy time series is the Chen model, because it has better accuracy values than the Song and Chissom models. Based on this research, it can be seen that the results of the prediction of the VAT value for the provision of services at PT Pertamina Hulu Rokan Zone 1, for the period July 2023 using the fuzzy time series Chen model method in second order obtained IDR 1,455,000,000 with a forecasting accuracy of 82.1%. In this way, PT PHR Zone 1 can maximize input VAT of IDR 1,455,000,000 so that the goal of minimizing the tax burden is achieved.

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.

Development of Accuracy for the Weighted Fuzzy Time Series Forecasting Model Using Lagrange Quadratic Programming

Limitation within the WFTS model, which relies on midpoints within intervals and linguistic variable relationships for assigning weights. This reliance can result in reduced accuracy, especially when dealing with extreme values during trend to seasonality transformations. This study employs the Weighted Fuzzy Time Series (WFTS) method to adjust predictive values based on actual data. Using Lagrange Quadratic Programming (LQP), estimated weights enhance the WFTS model. MAPE assesses accuracy as the model analyzes monthly IHSG closing prices from January 2017 to January 2023.The MAPE value of 0.61% results from optimizing WFTS with LQP. It utilizes a deterministic approach based on set membership counts in class intervals, continuously adjusting weights during fuzzification, minimizing the deviation between forecasted and actual data values.The Weighted Fuzzy Time Series Forecasting Model with Lagrange Quadratic Programming is effective in forecasting, indicated by a low MAPE value. This method evaluates each data point and adjusts weights, offering reliable investment insights for IHSG strategies..

A Comparative Study on Fuzzy Time Series Forecasting and Autoregressive Integrated Moving Average Models

To ensure the sustainable growth of coal production, it is necessary to analyse the growth, to ground the plans and management decisions on effective diagnostics and prediction of current and future situation at the coal production. This study presents a application of fuzzy time series forecasting methods, The new technique is applied to forecasting the coal production data using a fuzzy approach. For testing the methodology, statistical data on the coal production from 1980- to 2019. The Sturges rule is proposed to use as the universe of discourse. The intervals of variation of such indicators as growth rate are calculated when applying the approach to all defined fuzzy sets. The ARIMA model algorithm was applied by using the R - software to find the forecasted values. The forecasting results, obtained by the fuzzy time series method, are supposed to have more accuracy rate than time series model.

A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis

A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis

Comparison of the Chen and Sinsgh’s Fuzzy Time Series Methods in Forecasting Farmer Exchange Rates in Indonesia

Chen and Singh's Fuzzy Time Series Model is a forecasting method that uses the basi fuzzy logic in the process. The differences in the models are in the fuzzy logic relations. Chen's model uses Fuzzy Logical Relationship Groups. Meanwhile, the Singh model uses only Fuzzy Logical Relationships in the forecasting process. To find out the best model between the two models, forecasting the Farmer's Exchange Rate is carried out. Farmers' exchange rates are the option for observers of agricultural development in assessing the level of welfare of farmers in Indonesia. With changes in farmer exchange rates every month, it is necessary to forecast data in order to obtain an overview for the following month. Research used is applied research where the initial step is to study and analyze the theories related to our research, then colect the necessary data. The data used is data secondary data obtained online from the official website of the Badan Pusat Statistika (BPS). the forecasting results of the two models were compared using MAPE. The results of the comparison of the accuracy of the prediction accuracy of Chen and Singh's fuzzy time series models on farmers' exchange rates obtained Chen's MAPE fuzzy time series values ​​of 0.679% and Singh's fuzzy time series models of 0.354%. This means that the best forecasting model for farmer exchange rates in Indonesia is the Singh model.

Comparison of Gold Price Prediction Techniques Using Long Short Term Memory (LSTM) And Fuzzy Time Series (FTS) Method

Gold is a precious metal that has economic value and is often used as an investment tool. The demand for gold from day to day is increasing, because many know and think that gold can be used as ownership in the form of investment assets that have low risk. Therefore, it is necessary to predict the gold price to avoid losses. This study aims to predict the gold price using a machine learning architecture including deep learning, namely Long Short Term Memory (LSTM) and Fuzzy Time Series (FTS). Several trial processes were carried out in the training process and predict the LSTM and FTS models to get the best results. The data used in this experiment is real data from the period 15 September 2016 – 15 September 2021. The final results obtained from the LSTM method have an RMSE value of training data of 391.95 RMSE, and a value of test data 412.36 RMSE, and the FTS method has an RMSE value of 10449.115791541652