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Geleceğe ilişkin öngörüde bulunma ekonometrinin önemli konularından biridir. Öngörü yapmada Klasik zaman serisi yöntemlerinin kullanmak; doğrusallık, durağanlık, normal dağılma ve büyük örneklem gibi birçok istatistiksel varsayımın sağlanmasına gerek duymaktadır. Bununla birlikte, bulanık zaman serisi yöntemleri bu varsayımların hiçbirini gerektirmeyen parametrik olmayan istatistiksel yöntemler grubuna girmektedir. Bu nedenle, gerçek hayat zaman serilerinin öngörülmesinde sıklıkla tercih edilmektedir. Bu çalışmada öncelikle Türkiye’deki ikinci el konut satış verileri için klasik ARIMA(p,d,q) (Box ve Jenkıns, 1976) ile literatürde temel bulanık zaman serisi yöntemlerinden biri olan Chen (1996)’in bulanık mantık ilişkilere dayalı yöntemlerinin öngörü performansları karşılaştırılmıştır. Böylece, gelecekteki 1 ay, 4 ay ve 16 ay için yapılan öngörüler için, bulanık yaklaşımın klasik zaman serisi yaklaşımına göre çok daha iyi öngörü performansına sahip olduğu açıkça ortaya konulmuştur. Daha sonra, Chen (1996)’nın yöntemi ile Ağustos 2024 tarihine kadar olan ikinci el aylık konut sayıları kullanılarak Eylül 2024 ve Ekim 2024 için öngörüler yapılmış ve öngörülen değerlerin gerçekleşen değerlere oldukça yakın değerler elde edildiği saptanmıştır. Böylece, Chen (1996) yönteminin geleceği tahmin etmede iyi bir öngörü performansına sahip olduğu anlaşılmıştır. Chen (1996)’nın bulanık mantık ilişkilere dayalı yöntemi sadece birinci dereceden bir otoregresif model olan temel bir yöntem olmasına rağmen klasik zaman serisi yaklaşımına göre oldukça iyi öngörüler yapılabildiği gösterilmiştir. Çalışmamızda, ayrıca Chen (1996) yöntemi ile gelecekteki dönemlerde ikinci el konut satışlarına ilişkin öngörülerde bulunulmuştur. Elde edilen sonuçlara göre, Türkiye de Kasım 2024’te 109789 ve Aralık 2024’te 123814, 2024 yılında 987419 ve 2025 yılında 935827 ikinci el konut satılacağı öngörülmektedir.

Abstract 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.

Recently, traditional manufacturing industries have faced two serious gaps and problems in line with effective product-line sales forecasting methods to balance the negative impacts on the performance of the subjective experience, including (1) arbitrary judgment, such as growth rate of expectancy, manager's experiences, and historical sales data, may cause inaccurately predictive results and severe negative effects, and (2) sales forecasting is a key priority and challenge in the context of considerable product lines that have different properties and need specific models for supporting decision analytics. This study is motivated to propose an advanced hybrid model to utilize the advantages of variation for methods of fuzzy time series (FTS), exponential smoothing (ES), moving average (MA), and regression analysis (RA). To analyze the four product lines—stably growing product (SGP), declining product (DP), irregularly growing product (IGP), and special sales product (SSP)—this study is based on empirical sales data from a leading traditional manufacturer to accurately identify the high potentials of decisive key factors and objectively evaluate the model. Two evaluation standards—the mean absolute percentage error (MAPE) and root mean square error (RMSE), a parameter sensitivity analysis, and comparative analysis—are measured. After implementing the data from the case study, four key reports were conclusively identified. (1) Purely for the RMSE, the best one (10.32) is the ES method in the SGP line. (2) In the DP line, the better one is the RA(2) method, with a relatively low MAPE of 17.78% and RMSE of 26.46. (3) The FTS method is the best choice (MAPE 12.41% and RMSE 18.98) for the IGP line. (4) For the SSP line, the better one (MAPE 24.05 and RMSE 29.34) is the MA method. According to the above reports, although the proposed hybrid model has a general performance for the SSP line, it still has a superior predictor when compared to manager subjective prediction. Interestingly, the proposed model is rarely used, has a new trial with an innovative solution for the traditional manufacturer, and thus realizes applicable values. The study concludes with acceptable and satisfactory results and yields seven important findings and three managerial implications that significantly contribute to decision-making reference for complete sales-production planning for interested parties. Thus, this study benefits and values a conventional industry upgrade from novel application techniques. [ABSTRACT FROM AUTHOR]

Decision-making in many industries relies heavily on accurate forecasts, including the insurance sector. The Social Insurance System (SIS) in Egypt, operating under a fully funded paradigm, depends on reliable predictions to ensure effective financial planning. This research introduces a hybrid predictive model that combines fuzzy time series (FTS) Markov chains with the tree partition method (TPM) and difference transformation to forecast total pension benefits within Egypt's SIS. A key feature of the proposed model is its ability to optimize the partitioning process, resulting in the creation of nine intervals that reduce computational complexity while maintaining forecasting accuracy. These intervals were consistently applied across all fuzzy time series models for comparison. The model's performance is evaluated using established metrics such as MAPE, Thiels' U statistic, and RMSE. Additionally, prediction interval coverage probability (PICP) and mean prediction interval length (MPIL) are used to assess the quality of prediction intervals, with a 95% prediction interval serving as the baseline. The proposed model achieved a PICP of approximately 95%, indicating well-calibrated prediction intervals, although the MPIL of 424.5 reflects a wider uncertainty range. Despite this, the model balances coverage accuracy and interval precision effectively. The results demonstrate that the proposed model significantly outperforms traditional models like linear regression, ARIMA, and exponential smoothing and conventional FTS models like Song, Chen, Yu, and Cheng by achieving the lowest MAPE with the value of 11.8% for training and 10.65% for testing. This superior performance highlights the model's reliability and potential applicability to further forecasting tasks in the field of insurance and beyond. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development of accurate models to forecast load demand across different time horizons is challenging due to demand patterns and endogenous variables that affect short-term and long-term demand. This paper presents two contributions. First, it addresses the problem of the accuracy of the probabilistic forecasting model for short-term time series where endogenous variables interfere by emphasizing a low computational cost and efficient approach such as Granular Weighted Multivariate Fuzzy Time Series (GranularWMFTS) based on the fuzzy information granules method and a univariate form named Probabilistic Fuzzy Time Series. Secondly, it compares time series forecasting models based on algorithms such as Holt-Winters, Auto-Regressive Integrated Moving Average, High Order Fuzzy Time Series, Weighted High Order Fuzzy Time Series, and Multivariate Fuzzy Time Series (MVFTS) where this paper is based on Root Mean Squared Error, Symmetric Mean Absolute Percentage Error, and Theil's U Statistic criteria relying on 5% error criteria. Finally, it presents the concept and nuances of the forecasting approaches evaluated, highlighting the differences between fuzzy algorithms in terms of fuzzy logical relationship, fuzzy logical relationship group, and fuzzification in the training phase. Overall, the GranularWMVFTS and weighted MVFTS outperformed other evaluated forecasting approaches regarding the performance criteria adopted with a low computational cost. [ABSTRACT FROM AUTHOR]

Abstract 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.

Fuzzy Time Series (Fts) models are experiencing an increase in popularity due to their effectiveness in forecasting and modelling diverse and intricate time series data sets. Essentially these models use membership functions and fuzzy logic relation functions to produce predicted outputs through a defuzzification process. In this study, we suggested using a Second Order Type-1 fts (S-O T-1 F-T-S) forecasting model for the analysis of time series data sets. The suggested method was compared to the state-of-theart First Order Type 1 Fts method. The suggested approach demonstrated superior performance compared to the First Order Type 1 Fts method when applied to household consumption data from the Magene Regency in Indonesia, as measured by absolute percentage error rate (APER).

Abstract:In time series prediction tasks, the uncertainty of historical observations poses difficulties in forecasting. However, the forecasting methods based on fuzzy time series have unique advantages in dealing with data uncertainty. Probabilistic forecasting, on the other hand, can provide the distribution of the predicted target and quantify the uncertainty of the prediction results. Therefore, a fuzzy time series probabilistic forecasting method based on a probability weighting strategy is proposed to reduce the impact of uncertainty on the forecasting task. The proposed method builds a probability-weighted fuzzy time series prediction model using historical observations of the target variable, and refines the fuzzy rule base of the prediction model by introducing additional observations. Specifically, two operators with low computational cost are used to reconstruct the fuzzy logic relationships. The intersection operator is used to exclude the interfering information, while the union operator merges all information, resulting in two different sets of fuzzy logic relationship groups. The relationship group corresponding to the current observation value in two sets is the prediction for the fuzzy set in the next moment. Finally, the probability distribution of the next moment is output by defuzzification. Experimental results on publicly available time series data sets verify the accuracy and validity of this method, and the prediction accuracy is remarkably improved in comparison to the newly proposed PWFTS prediction method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fuzzy Time Series Forecasting (TSF) is an approach for dealing with uncertainty in time series data that uses fuzzy logic. The Hesitant Fuzzy Set (HFS) theory better emphasizes the chances of capturing fuzziness and uncertainty due to randomness than the classic fuzzy set theory. This study aims to improve the previously identified hesitant fuzzy TSF models by including various degrees of hesitation to improve forecasting performance. The goal is to deal with the issue of identifying a common membership grade when several fuzzification methods are available to fuzzify time series data. The proposed method utilizes trapezoidal and bell-shaped fuzzy membership functions for constructing HFSs. Ahesitant fuzzy weighted averaging operator is then applied to the Hesitant Fuzzy Elements (HEFs) to create fuzzy logical relations. The suggested technique is employed to forecast enrollment in the University of Alabama and Cancer Incidence Rates (CIRs) in India. The efficiency of the proposed forecasting approach is determined by rigorously comparing it to various computational fuzzy TSF methods in terms of error measurements like Root Mean Square Error (RMSE), Average Forecasting Error (AFE), and Mean Absolute Deviation (Mad). The validity of the proposed forecasting model is verified by using correlation coefficients, coefficients of determination, Tracking Signals (TSs), and Performance Parameters (PPs). The significance of improved accuracy in forecasted results is also confirmed using the two-tailed t-test. The study results revealed that the enhanced hesitant Fuzzy Time Series (FTS) model is more effective and accurate in forecasting the university enrolment of Alabama and the CIRs of India. [ABSTRACT FROM AUTHOR]

Decision-making in many industries relies heavily on accurate forecasts, including the insurance sector. The Social Insurance System (SIS) in Egypt, operating under a fully funded paradigm, depends on reliable predictions to ensure effective financial planning. This research introduces a hybrid predictive model that combines fuzzy time series (FTS) Markov chains with the tree partition method (TPM) and difference transformation to forecast total pension benefits within Egypt’s SIS. A key feature of the proposed model is its ability to optimize the partitioning process, resulting in the creation of nine intervals that reduce computational complexity while maintaining forecasting accuracy. These intervals were consistently applied across all fuzzy time series models for comparison. The model’s performance is evaluated using established metrics such as MAPE, Thiels’ U statistic, and RMSE. Additionally, prediction interval coverage probability (PICP) and mean prediction interval length (MPIL) are used to assess the quality of prediction intervals, with a 95% prediction interval serving as the baseline. The proposed model achieved a PICP of approximately 95%, indicating well-calibrated prediction intervals, although the MPIL of 424.5 reflects a wider uncertainty range. Despite this, the model balances coverage accuracy and interval precision effectively. The results demonstrate that the proposed model significantly outperforms traditional models like linear regression, ARIMA, and exponential smoothing and conventional FTS models like Song, Chen, Yu, and Cheng by achieving the lowest MAPE with the value of 11.8% for training and 10.65% for testing. This superior performance highlights the model’s reliability and potential applicability to further forecasting tasks in the field of insurance and beyond.

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

The literature frequently uses fuzzy inference methods for time series forecasting. In business and other situations, it is frequently necessary to forecast numerous time series. The q-Rung orthopair fuzzy set is a beneficial and competent tool to address ambiguity. In this research, a computational forecasting method based on q-Rung orthopair fuzzy time series has been created to deliver better prediction results to deal with situations containing higher uncertainty caused by large fluctuations in consecutive years' values in time series data and with no visualization of trend or periodicity. The main objective of this article is to handle time series forecasting with the usage of q-Rung orthopair fuzzy sets for things like floods, admission of students, number of patients, etc. After this, people can then manage issues that will arise in the future. Previously, there was a gap in determining the forecasting of data whose entire value of membership and non-membership exceeded 1. To fill this kind of gap, we used q-Rung orthopair fuzzy sets in time series forecasting. We also used numerous algebraic components for the q-Rung orthopair fuzzy time series, which has a union, max-min composition, cartesian product, and algorithm that are useful to calculate the method of data forecasting. Moreover, we also defined the algorithm and proposed MATLAB code that facilitates the execution of mathematical calculations, design, analysis, and optimization (structural and mathematical), and gives results with speed, correctness, and precision. At the end, we tested the model using historical student enrollment data and the annual peak discharge at Guddu Barrage. Furthermore, we calculated the error to get an idea of to what extent this method is suitable. [ABSTRACT FROM AUTHOR]

Non-stationary time series plays a prominent role in the analysis of performance time series of many real-world systems. Recently, fuzzy time series models have been extended to forecast non-stationary time series. Over different phases of time, performance time series may show drastic changes. Therefore, a non-stationary time series is partitioned according to different phases of time. These phases may be taken as weeks, months, or years. Over different phases of time, the universe of discourse, knowledge base, and rule base may vary. A common constraint in the modelling of time-variant fuzzy time series is their incapability to address the phase change of time in the model and accordingly incorporate the necessary changes in the universe of discourse, knowledge base, and rule base over different time phases. To address this issue, a Phase-cum-Time Variant Fuzzy Time Series Model (PTVFTS) is presented in this paper. The PTVFTS model is developed so that it will address the problem of phase change as well as the time variations within each phase simultaneously. The concept of the model rebuilding process is applied to handle changes over each phase and the modified parameter adaptation technique is used for the time variations within each phase. The developed model is applied to the daily closing price time series for the years 2017, 2018, 2019, 2020, 2021, and 2022 separately of stock market indices, NASDAQ, S&P 500, Dow Jones, and TAIEX. The comparison of the developed model is made with the time-variant fuzzy time series model known as the non-stationary fuzzy time series model (NSFTS), Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) model, Long Short-Term Memory (LSTM) model, and the classical Auto-Regressive Integrated Moving Average (ARIMA) model. The efficiency of the developed PTVFTS model is tested using forecasting metrics and statistical tests. The comparison shows that the developed model is more efficient in forecasting phase-cum-time variant non-stationary time series.

The literature frequently uses fuzzy inference methods for time series forecasting. In business and other situations, it is frequently necessary to forecast numerous time series. The q-Rung orthopair fuzzy set is a beneficial and competent tool to address ambiguity. In this research, a computational forecasting method based on q-Rung orthopair fuzzy time series has been created to deliver better prediction results to deal with situations containing higher uncertainty caused by large fluctuations in consecutive years' values in time series data and with no visualization of trend or periodicity. The main objective of this article is to handle time series forecasting with the usage of q-Rung orthopair fuzzy sets for things like floods, admission of students, number of patients, etc. After this, people can then manage issues that will arise in the future. Previously, there was a gap in determining the forecasting of data whose entire value of membership and non-membership exceeded 1. To fill this kind of gap, we used q-Rung orthopair fuzzy sets in time series forecasting. We also used numerous algebraic components for the q-Rung orthopair fuzzy time series, which has a union, max-min composition, cartesian product, and algorithm that are useful to calculate the method of data forecasting. Moreover, we also defined the algorithm and proposed MATLAB code that facilitates the execution of mathematical calculations, design, analysis, and optimization (structural and mathematical), and gives results with speed, correctness, and precision. At the end, we tested the model using historical student enrollment data and the annual peak discharge at Guddu Barrage. Furthermore, we calculated the error to get an idea of to what extent this method is suitable.

The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 1.24% for proposed method.

Minyak mentah merupakan komoditas dan sumber energi yang sangat dibutuhkan bagi pertumbuhan suatu negara. Harga minyak mentah mengalami peningkatan dan penurunan. Naiknya harga minyak mentah akan mempengaruhi perekonomian dan pasar keuangan. Sedangkan turunnya harga minyak mentah akan mengakibatkan masalah defisit anggaran yang serius bagi negara-negara pengekspor minyak. Oleh sebab itu perlu dilakukannya peramalan harga minyak mentah dunia untuk mengurangi dampak dari fluktuasi harga minyak mentah dunia tersebut. Metode peramalan yang dapat digunakan dalam meramalkan data time series harga minyak mentah dunia adalah dengan menggunakan metode fuzzy time series logika Singh. Data yang digunakan merupakan data sekunder yaitu data harga minyak mentah dunia WTI periode November 2014 hingga Juni 2022. Hasil peramalan data harga minyak mentah dunia dengan menggunakan metode tersebut kemudian diukur tingkat akurasinya menggunakan MAPE. Dari metode peramalan fuzzy time series logika Singh diperoleh nilai MAPE sebesar 0.30%. Berdasarkan hasil MAPE tersebut, peramalan harga minyak mentah dunia dengan model Singh mempunyai tingkat akurasi sangat bagus.

The development of accurate models to forecast load demand across different time horizons is challenging due to demand patterns and endogenous variables that affect short-term and long-term demand. This paper presents two contributions. First, it addresses the problem of the accuracy of the probabilistic forecasting model for short-term time series where endogenous variables interfere by emphasizing a low computational cost and efficient approach such as Granular Weighted Multivariate Fuzzy Time Series (GranularWMFTS) based on the fuzzy information granules method and a univariate form named Probabilistic Fuzzy Time Series. Secondly, it compares time series forecasting models based on algorithms such as Holt-Winters, Auto-Regressive Integrated Moving Average, High Order Fuzzy Time Series, Weighted High Order Fuzzy Time Series, and Multivariate Fuzzy Time Series (MVFTS) where this paper is based on Root Mean Squared Error, Symmetric Mean Absolute Percentage Error, and Theil’s U Statistic criteria relying on 5% error criteria. Finally, it presents the concept and nuances of the forecasting approaches evaluated, highlighting the differences between fuzzy algorithms in terms of fuzzy logical relationship, fuzzy logical relationship group, and fuzzification in the training phase. Overall, the GranularWMVFTS and weighted MVFTS outperformed other evaluated forecasting approaches regarding the performance criteria adopted with a low computational cost.

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..

The traditional adaptive weighted fusion algorithm ignores the spatial correlation between the network sensing data and has high fusion bias, significantly reducing network data transmission quality. The network perception data fusion algorithm based on fuzzy time series is proposed, and the network perception data prediction algorithm based on fuzzy time series is used. The network data value is calculated using first and two-order fuzzy relations during the training stage, and the trend value of the network data is obtained. The trend value in the prediction stage dynamically obtains the network sense. Based on the relationship, the fuzzy relation of knowledge data is used to predict the network perception data for the next time series. The network sensing data fusion algorithm based on the confidence matrix is used to fuse the data by predicting the spatial correlation between the data and filtering the noise of the abnormal data to the fusion results. The proposed algorithm’s high fusion accuracy and improved quality of network data transmission are demonstrated by experimental results.

Based on the double hierarchy linguistic term sets (DHLTS), a novel forecasting model is proposed considering both the internal fluctuation rules and the external correlation of different time series. The innovative aspects of this model consist of: (i) It can expresses more internal fluctuation and external correlation information, providing guarantees for improving the predictive performance of the model. (ii) The equivalent transformation function of DHLTS reduces the fuzzy granularity and improves the prediction accuracy. (iii) The application of similarity measures can extract the closest rules from historical states based on the distance operators of DHLTS. In addition, experiments on TAIEX considering the impact of the U.S. stock market and other data show that the model has good predictive performance. [ABSTRACT FROM AUTHOR]

Unloading activities directly impact the progress of earthmoving and filling projects, and a reliable multistep-ahead unloading duration prediction could help optimize equipment scheduling and improve operational efficiency. However, unloading prediction is greatly challenging owing to the complex uncertainties and nonlinearities implied in the unloading process, as well as the difficulty of modeling long-term temporal dependencies. Thus, this study devises a new fuzzy sequence-to-sequence network for unloading time forecasting. First, a quantization error-improved information granulation method is exploited to establish the fuzzy partition function. The global and localized distribution characteristics of unloading time are utilized to adaptively optimize the number and distribution of nonuniform fuzzy intervals. Then, periodic and recent branches were developed to model the variation of unloading time in multiple temporal granularities. In each branch, based on the encoder–decoder structure, the underlying gated recurrent units learn the sequence features collaborating with the attention mechanism to capture implicit long-term dependencies, mitigating the effects of error accumulation in multistep forecasting. Finally, the temporal information of different granularities is fused to form the final prediction. We evaluate the proposed model using an unloading dataset from a heavy infrastructure project in southwest China. We conducted geo-fencing and unloading operation analysis to extract the unloading information of different construction areas. The experimental results show that our method can generate high-quality multistep predictions for unloading duration, and exhibits superior performance compared with baseline models. The novel approach has great potential to support earthwork management in complex environments. [ABSTRACT FROM AUTHOR]

An improved fuzzy time series algorithm based on clustering is designed in this paper. The algorithm is successfully applied to short-term load forecasting in the distribution stations. Firstly, the K-means clustering method is used to cluster the data, and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division. On this basis, the data is fuzzed to form a fuzzy time series. Secondly, a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load, which is used to predict the short-term trend change of load in the distribution stations. Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are [−50, 20] and [−50, 30], while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are [−20, 15] and [−20, 25]. It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations. [ABSTRACT FROM AUTHOR]

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.

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.

With the development of machine learning algorithm and fuzzy theory, the fuzzy clustering algorithm based on time series has received more and more attention. Based on the time series theory and considering the correlation of data attributes, it proposes a novel multivariate fuzzy time series clustering method based on Slacks Based Measure (MFTS-SBM). Compared with traditional fuzzy clustering that it has the ability to deal with fuzziness and uncertainty, the proposed hybrid SBM clustering method employs with input and output items and considers the clustering results and the influencing factors of nonparametric frontier. Thus, it is important for data decision making because decision makers are interested in understanding the changes required to combine input variables in order to classify them into the desired clusters. The simulation experiment results of different samples are given to explain the use and effectiveness of the proposed hybrid SBM clustering method. Therefore, the hybrid method has strong theoretical significance and practical value.

Due to the limitations of traditional time series models in handling semantic values and small-scale data, the concept of fuzzy time series forecasting has been introduced in academia. This model performs exceptionally well on fuzzy datasets, prompting many researchers to delve into this field. The general process of fuzzy time series analysis consists of the following stages: 1) domain partitioning; 2) formation of fuzzy sets for fuzzifying data; 3) extraction of fuzzy relationships; and 4) forecasting and defuzzification. Domain partitioning and the extraction of fuzzy relationships have always been crucial components of fuzzy time series forecasting. Until now, neural networks have been less commonly applied in the step of determining fuzzy relationships. Some researchers have attempted to utilize the Pi-Sigma neural network for the determination of fuzzy relationships. However, due to the fixed network structure that Pi-Sigma neural networks cannot adapt to changes over time, it has been indicated that it is not a universal approximator. Its performance in handling complex dynamic time series has not been satisfactory. In this paper, we utilize Fuzzy C-Means Clustering (FCM) to partition the domain into unequal-length intervals and employ a high-order dynamic neural network known as Dynamic Ridge Polynomial Neural Network (DRPNN). This network can start with a small basic structure and gradually increase its structural complexity as learning progresses until it achieves the required task accuracy, which demonstrates superior performance in handling complex time series data. During the training process, we employ a novel gradient descent training algorithm with penalty terms. We conducted tests on this algorithm using nine real-world datasets and performed Friedman and Bonferroni-Dunn tests to ensure that the proposed algorithm exhibits statistical performance superiority compared to other methods in the literature. The results indicate that our algorithm outperforms those from other studies.

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. [ABSTRACT FROM AUTHOR]