A Novel Algorithm to Forecast Enrollment Based on Fuzzy Time Series

In this paper we propose a new method to forecast enrollments based on fuzzy time series. The proposed method belongs to the first order and time-variant methods. Historical enrollments of the University of Alabama from year 1948 to 2009 are used in this study to illustrate the forecasting process. By comparing the proposed method with other methods we will show that the proposed method has a higher accuracy rate for forecasting enrollments than the existing methods

Forecasting Enrollment Model Based on First-Order Fuzzy Time Series

This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature

High-order RTV-FUZZY time series forecasting model based on trend variation

Time series data principally involves four major components which are trend, cyclical, seasonal and irregular, that reflects the characteristics of the data. Ignoring the systematic analysis of patterns from time series components will affect forecasting accuracy. Thus, this paper proposes a high-order ratio trend variation (RTV) fuzzy time series model based on the trend pattern and variations in time series to deal with patterns within the time series data. RTV is used in the fuzzification process to deal with data that contains vagueness, uncertainty and impreciseness. Proper adjustment was also applied to handle the common issues in fuzzy time series model includes determination of length of interval, fuzzy logic relations (FLRs), assigning weight to each FLR and the defuzzification process. Empirical analysis was performed on enrollments data of Alabama University to assess the efficiency of the model. The performance of the proposed model was evaluated by comparing the average forecasting error rate and mean square error values with several fuzzy time series models in the literatures. Experimental results revealed that the proposed model was better than other fuzzy time series models. The use of RTV was able to grip the fuzziness in time series data and reduce the estimation of forecasting errors. In addition, this technique is capable to identify and describe the underlying structure that influence the occurrence of the uncertainty and high fluctuation of the phenomena under investigation

A refined approach for forecasting based on neutrosophic time series

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series

Rainfall Forecast in Different Methods of Trend Equations by Fuzzy Time Series

Fuzzy time series models have been put forward for Rainfall Prediction from many researchers around the globe. Fuzzy time series methods do not require any assumptions valid for classic time series approaches. The most important disadvantage of fuzzy time series approaches is that it needs subjective decisions, especially in fuzzification stage. This paper proposes a novel improvement of forecasting approach based on using first order fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical rainfall data of Trichy district. In this study reveals some feature of FTS predicting Rainfall and the results have been compared with other methods

A Fuzzy Time Series-Based Model Using Particle Swarm Optimization and Weighted Rules

During the last decades, a myriad of fuzzy time series models have been proposed in scientific literature. Among the most accurate models found in fuzzy time series, the high-order ones are the most accurate. The research described in this paper tackles three potential limitations associated with the application of high-order fuzzy time series models. To begin with, the adequacy of forecast rules lacks consistency. Secondly, as the model's order increases, data utilization diminishes. Thirdly, the uniformity of forecast rules proves to be highly contingent on the chosen interval partitions. To address these likely drawbacks, we introduce a novel model based on fuzzy time series that amalgamates the principles of particle swarm optimization (PSO) and weighted summation. Our results show that our approach models accurately the time series in comparison with previous methods

Fuzzy Time Series for Projecting School Enrolment in Malaysia

There are a variety of approaches to the problem of predicting educational enrolment.  However, none of them can be used when the historical data are linguistic values.  Fuzzy time series is an efficient and effective tool to deal with such problems. In this paper, the forecast of the enrolment of pre-primary, primary, secondary, and tertiary schools in Malaysia is carried out using fuzzy time series approaches. A fuzzy time series model is developed using historical dataset collected from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) from the year 1981 to 2018.  A complete procedure is proposed which includes: fuzzifying the historical dataset, developing a fuzzy time series model, and calculating and interpreting the outputs. The accuracy of the model are also examined to evaluate how good the developed forecasting model is. It is tested based on the value of the mean squared error (MSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD).  The lower the value of error measure, the higher the accuracy of the model.  The result shows that fuzzy time series model developed for primary school enrollments is the most accurate with the lowest error measure, with the MSE value being 0.38, MAPE 0.43 and MAD 0.43 respectively

A Fuzzy Time Series-Based Model Using Particle Swarm Optimization and Weighted Rules

During the last decades, a myriad of fuzzy time series models have been proposed in scientific literature. Among the most accurate models found in fuzzy time series, the high-order ones are the most accurate. The research described in this paper tackles three potential limitations associated with the application of high-order fuzzy time series models. To begin with, the adequacy of forecast rules lacks consistency. Secondly, as the model's order increases, data utilization diminishes. Thirdly, the uniformity of forecast rules proves to be highly contingent on the chosen interval partitions. To address these likely drawbacks, we introduce a novel model based on fuzzy time series that amalgamates the principles of particle swarm optimization (PSO) and weighted summation. Our results show that our approach models accurately the time series in comparison with previous methods

PERAMALAN JUMLAH PEMINAT PROGRAM STUDI MATEMATIKA FMIPA UNS MENGGUNAKAN RUNTUN WAKTU FUZZY PADA PENENTUAN INTERVAL DENGAN METODE BERBASIS RATA-RATA DAN PENGELOMPOKAN OTOMATIS

Setiap tahun Universitas Sebelas Maret (UNS) perlu melakukan perencanaan berhubungan dengan pengambilan keputusan manajerial UNS. Jumlah peminat di UNS tidak menentu setiap tahun sehingga pihak manajemen UNS tidak dapat menggunakan perencanaan anggaran sama seperti tahun sebelumnya. Oleh karena itu, perlu dilakukan peramalan jumlah peminat untuk membantu mengatasi masalah tersebut. Peramalan jumlah peminat dapat menggunakan metode runtun waktu fuzzy. Metode tersebut menggunakan prinsip-prinsip fuzzy dalam proses peramalannya. Penentuan interval merupakan langkah penting pada runtun waktu fuzzy karena dapat memengaruhi hasil peramalan. Penelitian ini membahas peramalan jumlah peminat Program Studi Matematika dengan penentuan interval runtun waktu fuzzy menggunakan metode berbasis rata-rata dan metode pengelompokan otomatis. Selanjutnya, metode dengan hasil root mean square error (RMSE) yang terkecil digunakan untuk meramalkan jumlah peminat tahun 2015. Pada penelitian ini data yang digunakan diubah menjadi himpunan fuzzy berdasarkan interval yang telah terbentuk dari semesta pembicaraan U. Langkah selanjutnya adalah membentuk kelompok relasi logika fuzzy untuk menentukan nilai peramalan. Berdasarkan hasil penelitian, nilai peramalan runtun waktu fuzzy menggunakan metode pengelompokan otomatis dengan subinterval 18 lebih akurat daripada metode berbasis rata-rata pada peramalan jumlah peminat Program Studi Matematika FMIPA UNS dari tahun 2003 sampai dengan 2014. Hal ini dapat menunjukkan nilai peramalan jumlah peminat pada tahun 2015 dengan menggunakan metode terbaik adalah 1705