Temperature Prediction Based on Fuzzy Time Series and MTPSO with Automatic Clustering Algorithm

2014 ◽  
Author(s):  
Yashvardhan Sharma ◽  
Sheetal Sisodia
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Fuzzy Time Series ◽  
Temperature Prediction ◽  
Automatic Clustering

scholarly journals Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Ya’nan Wang ◽  
Yingjie Lei ◽  
Xiaoshi Fan ◽  
Yi Wang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Intuitionistic Fuzzy Set ◽  
Time Series Forecasting ◽  
Approximate Reasoning ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Forecasting Model ◽  
New Model ◽  
Intuitionistic Fuzzy

Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

scholarly journals Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

2019 ◽  
Vol 11 (3) ◽  
pp. 793 ◽  
Author(s):  
Rashad Aliyev ◽  
Sara Salehi ◽  
Rafig Aliyev
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error

Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

scholarly journals Metode Automatic clustering-fuzzy logical relationships pada Peramalan Jumlah Penduduk di Kota Makassar

2019 ◽  
Vol 1 (2) ◽  
pp. 193
Author(s):  
Muhammad Abdy ◽  
Rahmat Syam ◽  
Elfira Haryanensi
Keyword(s):  
Time Series ◽  
Fuzzy Theory ◽  
Secondary Data ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Clustering ◽  
Forecasting Method ◽  
Fuzzy Logical

Abstrak. Penelitian ini merupakan penerapan metode automatic clustering-fuzzy logical relationships unruk meramalkan jumlah penduduk di Kota Makassar menggunakan data sekunder BPS Kota Makassar yang bertujuan memprediksi jumlah penduduk  tahun 2017-2021. Penelitian diawali dengan penentuan panjang interval, nilai tengah panjang interval, membuat relasi logika fuzzy, fuzzifikasi, defuzzifikasi, dan menghitung nilai error hasil ramalan dengan metode Mean Absolute Percentage Error. Hasil penelitian ini menunjukkan bahwa ramalan jumlah penduduk di Kota Makassar dari tahun 2016 ke 2017 meningkat, tahun 2017 sampai tahun 2019 menurun, dan pada tahun 2019-2021 meningkat dengan keakuratan yang sangat bagus.Kata kunci:Automatic clustering-fuzzy logical relationships, Fuzzy Time Series,TeoriFuzzyAbstract.This research is the application of the forecasting method of fuzzy time series which is the method of automatic clustering fuzzy-logical relationships in forecasting the population of Makassar City using secondary data from BPS Makassar city which aims to predicting the population in year 2017-2021. The discussion starting from the determination of the length of the interval, determining the value of the middle length interval, making relations of fuzzy logic, fuzzification, defuzzification, and calculating the error value of the forecasting result by using the method of Mean Absolute Percentage Error. The result of this research shows that the predictions of the population of Makassar City from 2016 to 2017 increased, from 2017 to 2019 decreased, and in 2019-2021 increased with the very good accuracy. Keywords:Automatic Clustering-Fuzzy Logical Relationships, Fuzzy Time Series,Fuzzy Theory

HEURISTIC BIVARIATE FORECASTING MODEL OF MULTI-ATTRIBUTE FUZZY TIME SERIES BASED ON FUZZY CLUSTERING

2012 ◽  
Vol 11 (01) ◽  
pp. 167-195 ◽  
Author(s):  
GUOFANG NAN ◽  
SHUAIYIN ZHOU ◽  
JISONG KOU ◽  
MINQIANG LI
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Bivariate Model ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
The Universe ◽  
Universe Of Discourse

Fuzzy time series has been applied to forecast various domain problems because of its capability to deal with vagueness and incompleteness inherent in data. However, most existing fuzzy time series models cannot cope with multi-attribute time series and remain too subjective in the partition of the universe of discourse. Moreover, these models do not consider the trend factor and the corresponding external time series, which are highly relevant to target series. In the current paper, a heuristic bivariate model is proposed to improve forecasting accuracy, and the proposed model applies fuzzy c-means clustering algorithm to process multi-attribute fuzzy time series and to partition the universe of discourse. Meanwhile, the trend predictors are extracted in the training phase and utilized to select the order of fuzzy relations in the testing phase. Finally, the proper full use of the external series to assist forecasting is discussed. The performance of the proposed model is tested using actual time series including the enrollments at the University of Alabama, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a sensor dataset. The experimental results show that the proposed model can be utilized for multi-attribute time series and significantly improves the average MAER to 1.19% when compared with other forecasting models.

scholarly journals A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Kamal S. Selim ◽  
Gihan A. Elanany
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Historical Data ◽  
New Method ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Critical Issues

Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches.

scholarly journals Peramalan KB Baru IUD dengan Metode Automatic Clustering and Fuzzy Logical Relationship

2018 ◽  
Vol 6 (2) ◽  
pp. 144
Author(s):  
Yunidar Ayu Pratama ◽  
Diah Indriani
Keyword(s):  
Time Series ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Increase ◽  
Logical Relationship ◽  
Automatic Clustering ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Government ◽  
Fuzzy Logical

This research aims for forecasting the number of participants Family Planning (FP) new IUD in East Java in 2017 method using Automatic Clustering And Fuzzy Logic Relationship (ACFLR). Make forecasting for the number of participants FP new IUD in the future important done. Forecasting will support the increase of the number of participants program FP new IUD as emphasized by the Government so that it can be used to take better decisions. Forecasting method of Automatic Clustering And Fuzzy Logical Relationship was chosen because the method has a higher degree of accuracy compared to the classical time series method and fuzzy time series. This study used secondary data recorded in Perwakilan BKKBN East Java in the form of the number of participants KB new IUD in East Java in 2011 to 2016. Based on the research results obtained forecasting the number of participants KB new IUD in 2017 is 65.616 participants with error rate prediction of 0.97% and the percentage increase in the number of participants from the previous year is 0.28%.

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