scholarly journals Prediksi Penjualan Produk Promo PT. Unilever, Tbk Menggunakan Metode Fuzzy Time Series

2020 ◽  
Vol 6 (2) ◽  
pp. 51-57
Author(s):  
Yehoshua Yehoshua ◽  
Kustanto Kustanto ◽  
Retno Tri Vulandari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Literature Study ◽  
Price Discounts ◽  
Input Design ◽  
Time Series Method ◽  
Item Data ◽  
Body Care

PT. Unilever is a multinational company headquartered in Rotterdam, the Netherlands (under the name Unilever N.V.), London, England (under the name Unilever pic.) And in Indonesia has a subsidiary, PT. Unilever, Tbk was established on December 5, 1933. Unilever produces food, drinks, cleaners, and also body care. Unilever is the third largest producer of household goods in the world, if based on the amount of revenue in 2012, behind P & G and Nestle. In forecasting products, it is often influenced by the sale of these products because there are also changes in sales for each period. Usually there is an increase in sales of these products which, among other things, is caused by price discounts, new products, one free one to buy promo, or a saving package from Unilever or from a rival company. Data collection method used by the author is a method of observation or directly observing the process of transmission, interview methods and literature study methods. While the method for processing data uses fuzzy time series algorithms, context diagrams, data flow diagrams, HIPO, relational diagram entities, data dictionary design, input design, output design, relation diagrams between tables, system implementation and testing. The method for implementation uses vb.net and Mysql. The results of this thesis are a system for calculating the forecasting amount of sales or sales of promo products for the following year. From this system, information on store data, item data, sales year history data, and forecasting data from fuzzy time series data will be displayed.. From rinso goods promotion data which have been calculated using fuzzy time series method which get MAPE value equal to 3,2%, so sales data for category of goods will experience increase based on calculation equal to 3,2%.

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals Information Systems of Forecasting Incidence Rates of Dengue Fever Disease Using Multivariate Fuzzy Time Series

2020 ◽  
Vol 202 ◽  
pp. 14005
Author(s):  
Ardian Fakhru Rosyad ◽  
Farikhin ◽  
Jatmiko Endro Suseno
Keyword(s):  
Time Series ◽  
Dengue Fever ◽  
Time Series Data ◽  
Incidence Rates ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Central Java ◽  
Time Series Method ◽  
Low Incidence ◽  
Central Java Province

Demak Regency is one of the regions in Central Java Province with a low incidence of Dengue Fever compared to other cities and districts. Even so, DHF control needs to be done to minimize the occurrence of dengue fever, because DHF is a fairly dangerous disease. One form of controlling the number of DHF events that is widely used is using forecasting models, one of them is using Fuzzy Time Series. The Multivariate Fuzzy Time Series (MFTS) model is a development of the Fuzzy Time Series model that can be used to forecast using time series data by using more than one variable for forecasting, compared to the Fuzzy Time Series method that usually using only one variable. Based on the research results obtained, the MFTS model has a fairly accurate MAPE value, wherein the best MAPE was at 3 years scenario with MAPE 10,728%.

A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

scholarly journals Fuzzy Time-Series Model of Electric Power Consumption

2000 ◽  
Vol 4 (3) ◽  
pp. 188-194 ◽  
Author(s):  
Kazuhiro Ozawa ◽  
◽  
’Takahide Niimura ◽  
Tomoaki Nakashima ◽  
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Electric Power ◽  
Time Series Data ◽  
Simple Procedure ◽  
Estimation Procedure ◽  
Fuzzy Time Series ◽  
Ar Model ◽  
Electric Power Consumption ◽  
Series Data

In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.

scholarly journals Prediksi Harga Jagung Menggunakan Metode Fuzzy Time Series Dengan Atau Tanpa Menggunakan Markov Chain

2021 ◽  
Vol 4 (1) ◽  
pp. 49-54
Author(s):  
Danung Nur Adli
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data

Penelitian ini dilakukan dengan tujuan memprediksi harga pakan jagung menggunakan salah satu model matematika yang disebut fuzzy time series. Data yang didapatkan yaitu data historis atau rentan waktu dari berbagai literasi seperti hargaweb.id, jagungbisi.com, dan BPS dari tahun 2020-2021, kuarter pertama. Data tersebut nantinya akan dijadikan bahan perhitungan. Data dianalisa menggunakan R Studio. Kemudian algoritma fuzzy time series. hasil penelitian fuzzy time series menghasilkan prediksi harga pada jagung menggunakan time series. Memprediksi harga akan cenderung berubah dari kisaran Rp/ 4.000-4.400,- yang mana tingkat error hanya ada di level 8,23.Logika fuzzy atau time series mampu menyajikan prediksi harga jagung pada tahun 2020-2021 dengan keakuratan dengan tingkat error 8.23% artinya tidak berbeda jauh. Kedepannya banyak model matematika yang bisa digunakan untuk memprediksi dari harga bahan baku atau aspek lainnya pada industri peternakan.

A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

2015 ◽  
Vol 06 (04) ◽  
pp. 1550041 ◽  
Author(s):  
Sanjay Kumar ◽  
Sukhdev Singh Gangwar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Financial Time Series ◽  
Intuitionistic Fuzzy Sets ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy ◽  
Forecasting Method

Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the decision system. In this research paper, fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation (nondeterminacy). To improve the forecasting accuracy, induced fuzzy sets are used to establish fuzzy logical relations. To verify the performance of the proposed model, it is implemented on one of the benchmarking time series data. Further, developed forecasting method is also tested and validated by applying it on a financial time series data. In order to show the accuracy in forecasting, the method is compared with other forecasting methods using different error measures.

Application of Fuzzy Sets and Shadowed Sets in Predicting Time Series Data

2018 ◽  
pp. 297-315
Author(s):  
Mahua Bose ◽  
Kalyani Mali
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Positional Information ◽  
Data Partitioning ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Shadowed Set ◽  
Shadowed Sets

In recent years, several methods for forecasting fuzzy time series have been presented in different areas, such as stock price, student enrollments, climatology, production sector, etc. Choice of data partitioning technique is a central factor and it highly influences the forecast accuracy. In all existing works on fuzzy time series model, cluster with highest membership is used to form fuzzy logical relationships. But the position of the element within the cluster is not considered. The present study incorporates the idea of fuzzy discretization and shadowed set theory in defining intervals and uses the positional information of elements within a cluster in selection of rules for decision making. The objective of this work is to show the effect of the elements, lying outside the core area on forecast. Performance of the presented model is evaluated on standard datasets.

A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data

2018 ◽  
pp. 49-56
Author(s):  
Haji A. Haji ◽  
Kusman Sadik ◽  
Agus Mohamad Soleh
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Data Generation ◽  
Data Set

Simulation study is used when real world data is hard to find or time consuming to gather and it involves generating data set by specific statistical model or using random sampling. A simulation of the process is useful to test theories and understand behavior of the statistical methods. This study aimed to compare ARIMA and Fuzzy Time Series (FTS) model in order to identify the best model for forecasting time series data based on 100 replicates on 100 generated data of the ARIMA (1,0,1) model.There are 16 scenarios used in this study as a combination between 4 data generation variance error values (0.5, 1, 3,5) with 4 ARMA(1,1) parameter values. Furthermore, The performances were evaluated based on three metric mean absolute percentage error (MAPE),Root mean squared error (RMSE) and Bias statistics criterion to determine the more appropriate method and performance of model. The results of the study show a lowest bias for the chen fuzzy time series model and the performance of all measurements is small then other models. The results also proved that chen method is compatible with the advanced forecasting techniques in all of the consided situation in providing better forecasting accuracy.

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