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.

Forecasting Inflation In Indonesia Using The Modified Fuzzy Time Series Cheng

Inflation is one of the most important indicators to analyze a country’s economy. Therefore, it is necessary to forecast the inflation rate. Forecasting can be done by various methods, one of which is Fuzzy Time Series Cheng. In this study, several modifications were made to the method used. The purpose of this study is to forecast using the Modified Fuzzy Time Series (FTS) Cheng method and determine the accuracy of the forecasting results obtained. The results of this study indicate that the Modified FTS Cheng method can be used in forecasting, either by determining the interval average-based or using the Sturges equation. Based on the results of the calculation of forecasting accuracy using Mean Absolute Percentage Error (MAPE), the accuracy for Modified FTS Cheng by determining the average-based interval for forecasting based on the current state and next state is 11.58% and 5.78%, respectively. Furthermore, the Modified FTS Cheng by determining the interval using the Sturges equation resulted in a MAPE value of 9.61% and a FTS Cheng of 7.54%. The MAPE value of each method is less than 10%, which means that the method has a very good performance, except for Modified FTS Cheng by determining the average-based interval for forecasting based on current state has good performance with MAPE values ​​between 10 % and 20%.  Inflasi merupakan salah satu indikator penting yang digunakan dalam menganalisa perekonomian di suatu negara. Oleh karena itu, perlu dilakukan peramalan terhadap tingkat inflasi. Peramalan dapat dilakukan dengan berbagai metode, salah satunya Fuzzy Time Series Cheng. Pada penelitian ini dilakukan beberapa modifikasi pada metode yang digunakan. Tujuan penelitian ini adalah melakukan peramalan menggunakan metode Fuzzy Time Series Cheng yang Dimodifikasi dan menentukan akurasi dari hasil peramalan yang diperoleh. Hasil dari penelitian ini menunjukkan bahwa metode Fuzzy Time Series Cheng Dimodifikasi dapat digunakan dalam melakukan peramalan, baik dengan penentuan interval berbasis rata-rata maupun menggunakan persamaan Sturges. Berdasarkan hasil perhitungan keakuratan peramalan menggunakan Mean Absolute Percentage Error (MAPE) diperoleh akurasi untuk Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval berbasis rata-rata untuk peramalan berdasarkan current state dan next state masing-masing sebesar 11,58% dan 5,78%. Selanjutnya, Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval meggunakan persamaan Sturges menghasilkan nilai MAPE sebesar 9,61% dan Fuzzy Time Series Cheng sebesar 7,54%. Nilai MAPE dari masing-masing metode kurang dari 10% yang berarti bahwa metode tersebut mempunyai kinerja yang sangat baik, kecuali Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval berbasis rata-rata untuk peramalan berdasarkan current state mempunyai kinerja yang baik dengan nilai MAPE berada antara 10% dan 20%

PERBANDINGAN METODE AVERAGE-BASED FUZZY TIME SERIES DAN WEIGHTED FUZZY TIME SERIES UNTUK MEMPREDIKSI EKSPOR MINYAK SAWIT MENTAH DI INDONESIA

Indonesia merupakan negara penghasil minyak sawit terbesar di dunia. Minyak sawit mentah atau Crude Palm Oil (CPO) merupakan produk dari minyak sawit yang menjadi salah satu sumber ekspor terbesar di Indonesia. Jumlah ekspor CPO ke seluruh dunia selalu mengalami fluktuasi setiap periodenya, sehingga diperlukan prediksi yang tepat agar pemerintah dapat mengalkulasikan devisa negara kedepannya. Tujuan penelitian ini adalah untuk mengetahui metode terbaik yang dapat digunakan antara metode Average-Based Fuzzy Time Series (ABFTS) dan Weighted Fuzzy Time Series (WFTS) dalam memprediksi ekspor CPO di Indonesia berdasarkan tingkat akurasi yang optimal. Data time series yang digunakan dalam penelitian ini adalah data ekspor CPO dari Indonesia ke seluruh dunia pada bulan Januari 2015 hingga Agustus 2022 dalam satuan kilogram dengan menggunakan uji keakuratan prediksi atau uji error yaitu MSE, MAD, dan MAPE. Penelitian ini membandingkan metode ABFTS, WFTS konstanta 1 (c=1), dan WFTS konstanta 2 (c=2). Hasil penelitian menunjukkan bahwa metode WFTS (c=1) memiliki tingkat error yang lebih rendah dibandingkan dengan metode prediksi lainnya, dimana metode WFTS (c=1) menghasilkan nilai MSE sebesar 16.674.974.689.632.000,00, MAD sebesar 102.780.270,41, dan MAPE sebesar 35,9%. Berdasarkan hal tersebut maka didapatkan akurasi prediksi sebesar 64,1% dan prediksi dapat dikatakan layak, sehingga metode WFTS dengan konstanta 1 layak digunakan untuk memprediksi ekspor CPO di Indonesia karena menghasilkan tingkat akurasi yang cukup tinggi. *** Indonesia is the largest palm oil producing country in the world. Crude Palm Oil (CPO) is a product of palm oil which is one of the largest export sources in Indonesia. The amount of CPO exports around the world always fluctuates every period, so an accurate prediction is needed so that the government can calculate the country's foreign exchange in the future. The purpose of this study was to find out the best method that can be used between the Average-Based Fuzzy Time Series (ABFTS) and Weighted Fuzzy Time Series (WFTS) methods in predicting CPO exports in Indonesia based on an optimal level of accuracy. The time series data used in this study is CPO export data from Indonesia to world from January 2015 to August 2022 in kilograms using prediction accuracy tests or error tests, namely MSE, MAD, and MAPE. This study compared the ABFTS method, WFTS constant 1 (c=1), and WFTS constant 2 (c=2). The results showed that the WFTS method (c=1) had a lower error rate compared to other prediction methods, where the WFTS method (c=1) produced MSE value of 16,674,974,689,632,000.00, MAD of 102,780,270.41 , and MAPE of 35.9%. Based on this, a prediction accuracy of 64.1% is obtained and the prediction can be said to be feasible, so that the WFTS method with a constant 1 is feasible to use to predict CPO exports in Indonesia because it produces a fairly high level of accuracy

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

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

Research of fuzzy time series model based on fuzzy entropy and fuzzy clustering

时间序列预测是通过对有限个历史观测样本进行分析来建立模型，并利用模型来解释数据之间的统计规律，以期达到控制和预报目的的一门学科，在众多领域中都有非常广泛的应用。对于时间序列的建模和预测，目前已经有了许多成熟的技术和方法，但传统时间序列预测方法往往依赖大量的历史数据，而在实际问题中由于不确定性的广泛存在导致历史数据往往是不完整的、不准确的和含糊的，因而限制了传统预测模型的应用。为了解决这些问题，Song和Chissom提出了模糊时间序列的概念，其主要是在传统时间序列预测的基础上引入了模糊理论，通过建立相应的模糊逻辑关系进行预测。由于模糊时间序列在处理数据的不确定性和模糊性方面上所显示的优势，关于...Time series forecasting is modeled by limited historical observations sample, it is a technology of using the model to explain the statistical regularity of data in order to achieve the purpose of control and forecast and having a wide range of applications in many fields. For time series modeling and forecasting, there are many mature technologies and methods. The traditional time series predicti...学位：理学硕士院系专业：数学科学学院_概率论与数理统计学号：1902010115250