Comparison of Fuzzy Time Series and ARIMA to Forecast Tourist Arrivals to Homestay in Pahang

Predictions of future events must be incorporated into the decision-making process. For tourism demand, forecasting is very important to help directors and investors to make decisions in operational, tactical, and strategic decisions. This study focuses on forecasting performance between Fuzzy Time Series and ARIMA to forecast the tourist arrivals in homestays in Pahang. The main objective of this study is to compare and identify the best method between Fuzzy Time Series and Autoregressive Integrated Moving Average (ARIMA) in forecasting the arrival of tourists based on the secondary data of tourist arrivals to homestay in Pahang from January 2015 to December 2018. ARIMA models are flexible and widely used in time-series analysis and Fuzzy Time Series which do not need large samples and long past time series. These two methods have been compared by using the mean square error (MSE) and mean absolute percentage error (MAPE) as the forecast measures of accuracy. The results show that Fuzzy Time Series outperforms the ARIMA. The lowest value of MSE and MAPE was obtained from using the Fuzzy Time Series method at values 2192305.89 and 11.92256, respectively.

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FUZZY TIME SERIES AND SARIMA MODEL FOR FORECASTING TOURIST ARRIVALS TO BALI

Jurnal Teknologi ◽

10.11113/jt.v57.1524 ◽

2012 ◽

Vol 57 (1) ◽

Cited By ~ 2

Author(s):

Maria Elena ◽

Muhamad Hisyam Lee ◽

Suhartono H. ◽

Hossein I. ◽

Nur Haizum Abd Rahman ◽

...

Keyword(s):

Time Series ◽

Classical Method ◽

Moving Average ◽

Forecast Accuracy ◽

Fuzzy Time Series ◽

Strategic Decisions ◽

Clear Cut ◽

Forecasting Method ◽

Forecasting Performance ◽

Future Events

Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision–making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical, and strategic decisions. Generally, in time series we can divide forecasting method into classical method and modern methods. Although recent studies show that the newer and more advanced forecasting techniques tend to result in improved forecast accuracy under certain circumstances, no clear–cut evidence shows that any one model can consistently outperform other models in the forecasting competition [1]. In this study, the forecasting performance between Box–Jenkins approaches of seasonal autoregressive integrated moving average (SARIMA) and four models of fuzzy time series has been compared by using MAPE, MAD and RMSE as the forecast measures of accuracy. The empirical results show that Chen's fuzzy time series model outperforms the SARIMA and the other fuzzy time series models.

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

Jurnal Gaussian ◽

10.14710/j.gauss.v9i3.28912 ◽

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

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First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables

Mathematical Problems in Engineering ◽

10.1155/2013/769125 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Cem Kocak

Keyword(s):

Time Series ◽

Fuzzy Logic ◽

Stock Exchange ◽

Life Time ◽

Fuzzy Time Series ◽

Moving Averages ◽

First Order ◽

Forecasting Method ◽

Forecasting Performance ◽

Time Series Method

Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR) variables, without any studies that also make use of moving averages (MAs) variables with the exception of only one study (Egrioglu et al. (2013)). In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs) models that are based not only on the lagged values of the time series (AR variables) but also on the lagged values of the error series (MA variables). To that end, a new first-order fuzzy ARMA(1,1) time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB) and Gold Prices time series in regards to forecasting performance.

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Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia

E3S Web of Conferences ◽

10.1051/e3sconf/202233500016 ◽

2022 ◽

Vol 335 ◽

pp. 00016

Author(s):

Osfar Sjofjan ◽

Danung Nur Adli

Keyword(s):

Time Series ◽

Markov Chain ◽

Secondary Data ◽

Fuzzy Time Series ◽

Percentage Error ◽

Mean Square ◽

Forecast Method ◽

Short Term ◽

Bird Nest ◽

Markov Chain Method

Edible bird nest (EBN) were traditional medicine consumed by the Tiongkok. This study compared two-algorithm method. Fuzzy time series and Markov chain as forecast method the number of bird nest exported from Indonesia. The secondary data between 2012 and 2018 were from Bureau Central Statistic (BPS). The scope using in this study were bird nest between 2012 until 2018, with a unit of volume kilograms (Kg). Used secondary export data, collected from BPS of Indonesia. Data were analysed using Fuzzy Time Series with and without Markov Chain using R Studio. The result showed that Fuzzy Time Series with and without Markov Chain method performs better in the forecasting ability in short-term period prediction and the values of Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE) tends to be smaller than the Fuzzy Time Series without Markov Chain. It can be concluded the number of exported can be used Fuzzy time series.

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PREDICTION MODEL FOR THE NUMBER OF ARI CASES IN CHILDREN IN SURABAYA USING ARIMA METHOD

Jurnal Biometrika dan Kependudukan ◽

10.20473/jbk.v9i1.2020.18-26 ◽

2020 ◽

Vol 9 (1) ◽

pp. 18

Author(s):

Alvin Zulhazmi Priambodo ◽

Mahmudah Mahmudah

Keyword(s):

Time Series ◽

Respiratory Infections ◽

Health Information System ◽

Percentage Error ◽

Acute Respiratory Infections ◽

Future Events ◽

Planning Decision ◽

Time Series Method ◽

The Subject ◽

The Mean

Forecasting is an important element in planning decision-making related to estimating future events. Forecasting techniques that are often developed and used today are the Time Series. Time series is a measurement of events through the stages of time in hours, days, months, and years format. This research uses the ARIMA time series method. The ARIMA method is used to model acute respiratory infections (ARI) in children. The best model is determined using the smallest error through the Mean Absolute Percentage Error (MAPE). The study aims to predict the number of ARI cases in children in Surabaya. This research is an unobtrusive/nonreactive research. The researcher conditioned the subjects to not being aware that the subject is being studied and therefore, left the subject uninterrupted. The data used was the number of ARI cases in children from January 2014 to December 2018. The data was obtained from the monthly report of the Health Information System Unit (HIS) of the Surabaya Health Office. The conclusion from this study showed that the ARIMA method obtained the best model results, namely ARIMA (2,1,2) with a MAPE value of 15.024. Forecasting results fluctuated and a downward trend in the case of ARI children in Surabaya. In certain months, the number of acute respiratory infections has increased significantly, including in February and March.

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Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

Sustainability ◽

10.3390/su11030793 ◽

2019 ◽

Vol 11 (3) ◽

pp. 793 ◽

Cited By ~ 5

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.

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Pengujian Metode Fuzzy Time Series Chen dan Hsu Untuk Meramalkan Nilai Indeks Bursa Saham Syariah Di Jakarta Islamic Index (JII)

Wiga Jurnal Penelitian Ilmu Ekonomi ◽

10.30741/wiga.v7i2.157 ◽

2017 ◽

Vol 7 (2) ◽

pp. 108-124

Author(s):

Rizka Zulfikar ◽

Prihatini Ade`Mayvita

Keyword(s):

Time Series ◽

Stock Market ◽

Secondary Data ◽

Fuzzy Time Series ◽

Stock Index ◽

Series Method ◽

Forecasting Error ◽

Time Series Method ◽

The Difference ◽

Market Indexes

This research is an empirical study to tested the accuracy of Chen and Hsu’s Fuzzy Time Series Method used to forecast sharia market stock index in Jakarta Islamic Index. The data used in this research are secondary data consists of daily stock market indexes during 23 November 2016 to 14 July 2017. Chen dan Hsu’s Fuzzied Series Method used in this research has the smallest MSE (Mean Square Error) and AFER (Average Forecasting Error Rate) value rather than others method such as Song and Chrissom (1993). Song and Chrissom (1994), Chen (1996), Hwang, Chen and Lee (1998), Huarng (2001) and Chen (2002). To tested the accuracy of the Chen’s dan Hsu’s Fuzzied Series. Method researcher has to do 5 (five) steps such as (1) Determine lag between historical data, interval and The Universe Data (U), (2) Distributing Data into The Unniverse, (3) Define The Fuzzy Set, (4) Determine The Fuzzy Logical Relationship (FLR), and (5) Analyse the Difference between data. There are 3 (three) rules in Chen’s dan Hsu’s Fuzzied Series Method based on the Difference and FLR. The result of this research is Chen dan Hsu’s Fuzzied Series Method has MSE = 1.88 and AFER =0.006% and it can be used to make forecasting on value and trend sharia stock market in Jakarta Islamic index.

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

Journal of Mathematics, Computations, and Statistics ◽

10.35580/jmathcos.v1i2.9242 ◽

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

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Model Heuristic Time Invariant Fuzzy Time Series dan Regresi Untuk Prediksi Laba dan Analisis Variabel yang Mempengaruhi

JURNAL SISTEM INFORMASI BISNIS ◽

10.21456/vol6iss2pp144-153 ◽

2016 ◽

Vol 6 (2) ◽

pp. 144

Author(s):

Ica Admirani ◽

Rachmat Gernowo ◽

Suryono Suryono

Keyword(s):

Time Series ◽

Prediction Error ◽

Fuzzy Time Series ◽

Actual Data ◽

Percentage Error ◽

Time Series Method ◽

Past Data ◽

Administrative Expenses ◽

Time Invariant ◽

Fuzzy Logical

Model of prediction with fuzzy time series method has ability to capture the pattern of past data to predict the fu ture of data does not need a complicated system, making it easier to use. The research aims to built prediction system using model of heuristic time invariant fuzzy time series and multiple linear regression to predict profit and analysis of variables that affect profit. Profit forecasting aims to determine the company's prospects in the future in order to remain exist in doing its business. The variables that use in the modelling are profit as the dependent variable, and sales, cost of goods sold, general and administrative expenses, selling and marketing expenses and interest income as the indepent variables. Profit forecasting modelling begins by defining universe of discourse and interval actual data of profit, then determine fuzzy set and actual data fuzzified. Furthermore, fuzzy logical relationship and fuzzy logical relationships group to fuzzified data. The prediction process consist of two prediction phase there are training phase aimed to determine trend predictor and testing phase to determine prediction results. By using 24 profit data samples resulted prediction error by using Mean Absolute Percentage Error is 11,64% and added 13 data for testing obtained prediction error is 22,27%. In analysis of variables that affect profit is known that sales variable most effect on profit than other variables with a regression coefficient 0.976.

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PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS

Jurnal Informatika Polinema ◽

10.33795/jip.v6i3.314 ◽

2020 ◽

Vol 6 (3) ◽

pp. 29-36

Author(s):

Deddy Kusbianto ◽

Agung Pramudhita ◽

Nurhalimah

Keyword(s):

Time Series ◽

Mean Square Error ◽

Moving Average ◽

Mean Absolute Percentage Error ◽

Fuzzy Time Series ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Mean Square ◽

Absolute Deviation ◽

Absolute Percentage Error

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series

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