Prediksi Curah Hujan Menggunakan Metode Adaptive-Expectation Based Multi-Attribute Fuzzy Time Series

Prediksi curah hujan sangat dibutuhkan dalam meningkatkan hasil produksi tanaman pangan pada suatu wilayah, salah satunya pada area Tengger. Kesalahan dalam memprediksi curah hujan dapat mengakibatkan kesalahan saat menentukan masa tanam, dan jenis tanaman yang tepat. Agar menghasilkan prediksi curah hujan dengan tingkat kesalahan sedikit, penelitian ini menggunakan metode Adaptive-Expectation Based Multi-Attribute Fuzzy Time Series. Metode tersebut telah terbukti mampu memprediksi closing price pada Taiwan Stock Exchange Capitalization Weighted Stock Index (TAEIX) dengan tingkat kesalahan lebih sedikit dibandingkan metode Fuzzy Time Series Chen. Penelitian ini akan menghasilkan prediksi curah hujan di empat kecamatan area Tengger yaitu Puspo, Sumber, Tosari, dan Tutur. Dari hasil pengujian 36 data pada tahun 2014, didapatkan nilai Mean Square Error (MSE) terbaik senilai 28,0470 pada kecamatan Tosari

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution

Neutrosophic soft sets forecasting model for multi-attribute time series

Traditional time series forecasting models mainly assume a clear and definite functional relationship between historical values and current/future values of a dataset. In this paper, we extended current model by generating multi-attribute forecasting rules based on consideration of combining multiple related variables. In this model, neutrosophic soft sets (NSSs) are employed to represent historical statues of several closely related attributes in stock market such as volumes, stock market index and daily amplitudes

Optimization of markov weighted fuzzy time series forecasting using genetic algorithm (GA) AND particle Swarm Optimization (PSO)

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic relationships. The main challenge of the MWFTS method is the absence of standardized rules for determining partition intervals. This study compares the MWFTS model to the partition methods Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clusteringParticle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves maximizing the interval length following the FKM procedure. The proposed method was applied to Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of 30,638 was reduced to 24,863

A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

Peramalan Harga Minyak Mentah Di Indonesia Dengan Menggunakan Metode Fuzzy Time Series

Meramalkan pergerakan harga minyak sangat penting bagi pelaku bisnis dalam pasar energy. Hal ini dikarenakan minyak bumi merupakan input vital dalam proses produksi industri, terutama untuk menghasilkan listrik, menjalankan mesin produksi dan mengangkut hasil produksi ke pasar. Mengingat peranannya yang vital tersebut, maka dampak yang timbul akibat fluktuasi harga minyak akan berpengaruh terhadap bidang lain, diantaranya pertumbuhan ekonomi, laju inflasi, jumlah uang beredar, nilai tukar riil rupiah terhadap US dolar dan suku bunga.Data harga minyak mentah Indonesia merupakan data time series. Dikatakan time series dikarenakan data harga minyak mempunyai interval waktu yang sama dan diamati pada suatu periode tertentu. Selain itu data harga minyak memiliki subjektivitas terhadap pengkategorian harga minyak. Subjektivitas terhadap harga minyak merupakan nilai linguistik. Oleh sebab itu, dibutuhkan proses peramalan dengan mengkaitkan time series dan teori fuzzy. Sehingga pada penelitian ini akan dikembangkan metode Fuzzy Time Series Markov Chain Model untuk meramalkan harga minyak mentah Indonesia. Fuzzy time series menggabungkan sikap subjektif orang dan nilai-nilai obyektif historis dapat membantu memecahkan masalah peramalan. Dalam penelitian ini, data yang digunakan adalah data harian harga minyak mentah Indonesia dari tahun 2010 hingga 2014. Analisa model peramalan yang sesuai dengan harga minyak mentah Indonesia dimana selama ini selalu mengalami fluktuasi dilakukan dengan pengujian proporsi data, pengujian kinerja model, serta pengujian perbandingan peramalan dengan metode FTS Markov Chain dengan FTS biasa. Hasil pengujian menunjukkan bahwa model peramalan yang tepat untuk data harga minyak mentah Indonesia adalah dengan menggunakan metode peramalan Fuzzy Time Series Markov Chain Model, dengan proporsi data 70:30, yang menghasilkan eror MAPE SLC sebesar 2.35%, Arjuna 2.53%, Attaka 2.22%, Cinta 2.59%, Duri 2.44%, Widuri 2.56%, Belida 2.45%, dan Senipah Condensate sebesar 2.36%. ========================================================================================================= Forecasting the movement of oil prices is very important for businessmen in the energy market. This is because oil is a vital input in the process of industrial production, mainly to generate electricity, run the machine production and transporting produce to market. Considering the vital role these impacts caused by fluctuations in oil prices will affect other fields, such as economic growth, inflation rate, the amount of money in circulation, the real exchange rate of the rupiah against the US dollar and interest rates. Indonesia crude oil price data is the data time series. Time series is said to be due to the oil price data has the same time interval and observed in a given period. Besides oil price data have subjectivity against this oil prices. Subjectivity against oil price is the value of Linguistics. Therefore, it takes the process of forecasting time series and linked with the theory of fuzzy. So this will be developed on the research method of Fuzzy Time Series Markov Chain Model for predicting the price of crude oil Indonesia. Fuzzy time series combines the subjective attitude of the person and the objective historical values can help solve the problem of forecasting. In this study, the data used is the daily crude oil price data for Indonesia from 2010 to 2014. Analysis of forecasting model which corresponds to the price of crude oil which Indonesia has always been experiencing fluctuations in the testing done by the proportion of data, testing the performance of the viii model, as well as comparison testing of forecasting method with FTS Markov Chain with FTS. The test results show that the proper forecasting model for Indonesia crude oil price data is by using Fuzzy Time Series forecasting method of Markov Chain Model, with the proportion of 70:30 data, which produces error MAPE SLC of 2.35% 2.53%, Arjuna, Attaka 2.22%, love 2.59% 2.44%, thorns, Widuri 2.56%, Belida 2.45%, and Senipah Condensate of 2.36%

Using Particle Swarm Optimization for Market Timing Strategies

Market timing is the issue of deciding when to buy or sell a given asset on the market. As one of the core issues of algorithmic trading systems, designers of such system have turned to computational intelligence methods to aid them in this task. In this thesis, we explore the use of Particle Swarm Optimization (PSO) within the domain of market timing.nPSO is a search metaheuristic that was first introduced in 1995 [28] and is based on the behavior of birds in flight. Since its inception, the PSO metaheuristic has seen extensions to adapt it to a variety of problems including single objective optimization, multiobjective optimization, niching and dynamic optimization problems. Although popular in other domains, PSO has seen limited application to the issue of market timing. The current incumbent algorithm within the market timing domain is Genetic Algorithms (GA), based on the volume of publications as noted in [40] and [84]. In this thesis, we use PSO to compose market timing strategies using technical analysis indicators. Our first contribution is to use a formulation that considers both the selection of components and the tuning of their parameters in a simultaneous manner, and approach market timing as a single objective optimization problem. Current approaches only considers one of those aspects at a time: either selecting from a set of components with fixed values for their parameters or tuning the parameters of a preset selection of components. Our second contribution is proposing a novel training and testing methodology that explicitly exposes candidate market timing strategies to numerous price trends to reduce the likelihood of overfitting to a particular trend and give a better approximation of performance under various market conditions. Our final contribution is to consider market timing as a multiobjective optimization problem, optimizing five financial metrics and comparing the performance of our PSO variants against a well established multiobjective optimization algorithm. These algorithms address unexplored research areas in the context of PSO algorithms to the best of our knowledge, and are therefore original contributions. The computational results over a range of datasets shows that the proposed PSO algorithms are competitive to GAs using the same formulation. Additionally, the multiobjective variant of our PSO algorithm achieve statistically significant improvements over NSGA-II

Collected Papers (on Neutrosophic Theory and Applications), Volume VI

This sixth volume of Collected Papers includes 74 papers comprising 974 pages on (theoretic and applied) neutrosophics, written between 2015-2021 by the author alone or in collaboration with the following 121 co-authors from 19 countries: Mohamed Abdel-Basset, Abdel Nasser H. Zaied, Abduallah Gamal, Amir Abdullah, Firoz Ahmad, Nadeem Ahmad, Ahmad Yusuf Adhami, Ahmed Aboelfetouh, Ahmed Mostafa Khalil, Shariful Alam, W. Alharbi, Ali Hassan, Mumtaz Ali, Amira S. Ashour, Asmaa Atef, Assia Bakali, Ayoub Bahnasse, A. A. Azzam, Willem K.M. Brauers, Bui Cong Cuong, Fausto Cavallaro, Ahmet Çevik, Robby I. Chandra, Kalaivani Chandran, Victor Chang, Chang Su Kim, Jyotir Moy Chatterjee, Victor Christianto, Chunxin Bo, Mihaela Colhon, Shyamal Dalapati, Arindam Dey, Dunqian Cao, Fahad Alsharari, Faruk Karaaslan, Aleksandra Fedajev, Daniela Gîfu, Hina Gulzar, Haitham A. El-Ghareeb, Masooma Raza Hashmi, Hewayda El-Ghawalby, Hoang Viet Long, Le Hoang Son, F. Nirmala Irudayam, Branislav Ivanov, S. Jafari, Jeong Gon Lee, Milena Jevtić, Sudan Jha, Junhui Kim, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, Songül Karabatak, Abdullah Kargın, M. Karthika, Ieva Meidute-Kavaliauskiene, Madad Khan, Majid Khan, Manju Khari, Kifayat Ullah, K. Kishore, Kul Hur, Santanu Kumar Patro, Prem Kumar Singh, Raghvendra Kumar, Tapan Kumar Roy, Malayalan Lathamaheswari, Luu Quoc Dat, T. Madhumathi, Tahir Mahmood, Mladjan Maksimovic, Gunasekaran Manogaran, Nivetha Martin, M. Kasi Mayan, Mai Mohamed, Mohamed Talea, Muhammad Akram, Muhammad Gulistan, Raja Muhammad Hashim, Muhammad Riaz, Muhammad Saeed, Rana Muhammad Zulqarnain, Nada A. Nabeeh, Deivanayagampillai Nagarajan, Xenia Negrea, Nguyen Xuan Thao, Jagan M. Obbineni, Angelo de Oliveira, M. Parimala, Gabrijela Popovic, Ishaani Priyadarshini, Yaser Saber, Mehmet Șahin, Said Broumi, A. A. Salama, M. Saleh, Ganeshsree Selvachandran, Dönüș Șengür, Shio Gai Quek, Songtao Shao, Dragiša Stanujkić, Surapati Pramanik, Swathi Sundari Sundaramoorthy, Mirela Teodorescu, Selçuk Topal, Muhammed Turhan, Alptekin Ulutaș, Luige Vlădăreanu, Victor Vlădăreanu, Ştefan Vlăduţescu, Dan Valeriu Voinea, Volkan Duran, Navneet Yadav, Yanhui Guo, Naveed Yaqoob, Yongquan Zhou, Young Bae Jun, Xiaohong Zhang, Xiao Long Xin, Edmundas Kazimieras Zavadskas