Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

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.

Download Full-text

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

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1184112 ◽

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.

Download Full-text

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

Download Full-text

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

Handbook of Research on Investigations in Artificial Life Research and Development - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5396-0.ch014 ◽

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.

Download Full-text

A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT

Kursor ◽

10.28961/kursor.v9i4.167 ◽

2019 ◽

Vol 9 (4) ◽

Author(s):

Bagus Dwi Saputra

Keyword(s):

Time Series ◽

Markov Chain ◽

Time Series Data ◽

Markov Chain Model ◽

Fish Farming ◽

Fuzzy Time Series ◽

Series Data ◽

Percentage Error ◽

Chain Model ◽

Price Fluctuations

Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.

Download Full-text

PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017)

Jurnal Gaussian ◽

10.14710/j.gauss.v8i4.26752 ◽

2019 ◽

Vol 8 (4) ◽

pp. 518-529

Author(s):

Setya Adi Rahmawan ◽

Diah Safitri ◽

Tatik Widiharih

Keyword(s):

Time Series ◽

Time Series Data ◽

High Order ◽

Training Data ◽

Fuzzy Time Series ◽

Series Data ◽

Percentage Error ◽

First Order ◽

Rice Prices ◽

Integrated Time Series

Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

Download Full-text

WATER CONSUMPTION PREDICTION USING FUZZY TIME SERIES - A CASE STUDY IN PRIVATE COMPANY OF TANGERANG DISTRICT INDONESIA

J TI UNDIP JURNAL TEKNIK INDUSTRI ◽

10.14710/jati.15.3.203-208 ◽

2020 ◽

Vol 15 (3) ◽

pp. 203-208

Author(s):

Diah Septiyana

Keyword(s):

Time Series ◽

Water Consumption ◽

Time Series Data ◽

Fuzzy Time Series ◽

Series Data ◽

Percentage Error ◽

Private Company ◽

Water Utility ◽

Demand Prediction ◽

Consumption Prediction

Consumption of water in the Tangerang Regency continuously increases from year to year due to the increasing population and birth rates an average increase of 3% every year. So, the water demand prediction to be important to meet customer or community needs. The private water utility company needs to use a new method for predicting future monthly water consumption values and improves accuracy when forecasting time series using a visibility graph and presents to make more accurate predictions. In this study, we aim to measure the trend analysis volume of water consumption prediction by Fuzzy Time Series versus actual usage volume. Fuzzy Time Series (FTS) is a concept plan method that uses fuzzy logic that is able to provide predictions (estimates) of time series data analysis for the next several periods. Mean Absolute Percentage Error (MAPE) is obtained for different configurations of the input sets and of the FTS model structure. From the results of the average value error accuracy was only 4.5% using FTS Chen Method and included in the low category and water consumption actual versus prediction with the FTS Chen method shown related stable.

Download Full-text

Peramalan Data Kunjungan Wisatawan Mancanegara ke Indonesia menggunakan Fuzzy Time Series

Jurnal Edukasi dan Penelitian Informatika (JEPIN) ◽

10.26418/jp.v5i1.31074 ◽

2019 ◽

Vol 5 (1) ◽

pp. 18

Author(s):

Indra Jiwana Thira ◽

Nissa Almira Mayangky ◽

Desiana Nur Kholifah ◽

Imanuel Balla ◽

Windu Gata

Keyword(s):

Time Series ◽

Time Series Data ◽

Mean Absolute Percentage Error ◽

Fuzzy Time Series ◽

Series Data ◽

Percentage Error ◽

Absolute Percentage Error

Wisatawan mancanegara memegang peranan penting terhadap pertumbuhan ekonomi dari sektor pariwisata. Untuk meningkatkan kunjungan wisatawan mancanegara perlu dilakukan pembangunan yang berkelanjutan pada sektor pariwisata. Pembangunan yang dilakukan harus sejalan dengan tren pertumbuhan kunjungan wisatawan mancanegara agar pembangunan tepat sasaran, efektif dan efisien. penelitian ini bertujuan untuk meramalkan kunjungan wisatawan mancanegara ke Indonesia menggunakan metode Fuzzy Time Series. Data historis yang digunakan adalah data kunjungan wisatawan mancanegara ke Indonesia periode Januari Tahun 2013 sampai dengan Desember Tahun 2017 dari Badan Pusat Statistik (BPS). Implementasi Fuzzy Time Series pada data historis menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 4,42 % dengan tingkat kesalahan tertinggi sebesar sebesar 18,05% pada Januari 2014 dan kesalahan terendah sebesar 0,04% pada Mei 2017. Hasil tersebut menunjukan bahwa penggunakan Fuzzy Time Series pada peramalan data kunjungan wisatawan mancanegara ke Indonesia memiliki hasil yang sangat baik.

Download Full-text

Multi-Factor Fuzzy Time Series Model Based on Stock Volatility for Forecasting Taiwan Stock Index

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.211-212.1119 ◽

2011 ◽

Vol 211-212 ◽

pp. 1119-1123 ◽

Cited By ~ 3

Author(s):

Ching Hsue Cheng ◽

Jing Wei Liu ◽

Tzu Hsuan Lin

Keyword(s):

Time Series ◽

Time Series Data ◽

Stock Exchange ◽

Time Series Model ◽

Fuzzy Time Series ◽

Stock Index ◽

Series Data ◽

Stock Volatility ◽

Taiwan Stock Exchange ◽

Proposed Model

Fuzzy time series have in recent years drawn many scholars' attention due to their ability can handle the time series data with incomplete, imprecise and ambiguous pattern. However, most traditional time series models employed only single variable (stock index) in forecasting, yet ignored some factors that would also affect the stock volatility. Therefore, this paper proposes a novel forecasting model using multi-factor fuzzy time series model to forecast TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index). Multi-factor fuzzy time series model is composed of three main components: stock index, trading volume and interactions between two stock markets. In order to evaluate the performance of the proposed model, the transaction records of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index) and NASDAQ(National Association of Securities Dealers Automated Quotations) from 2000/01/04 to 2003/12/31 are used as experimental dataset and the root mean square error (RMSE) as evaluation criterion. The results show that the proposed model outperforms the listing models in accuracy for forecasting Taiwan stock market.

Download Full-text

Distance variable improvement of time-series big data stream evaluation

Journal Of Big Data ◽

10.1186/s40537-020-00359-w ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ari Wibisono ◽

Petrus Mursanto ◽

Jihan Adibah ◽

Wendy D. W. T. Bayu ◽

May Iffah Rizki ◽

...

Keyword(s):

Time Series ◽

Standard Deviation ◽

Standard Method ◽

Time Series Data ◽

Series Data ◽

Percentage Error ◽

Traffic Demand ◽

Incremental Model ◽

Chernoff Bound ◽

The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

Download Full-text

An improved framework to predict river flow time series data

PeerJ ◽

10.7717/peerj.7183 ◽

2019 ◽

Vol 7 ◽

pp. e7183 ◽

Cited By ~ 1

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Ishfaq Ahmad ◽

Muhammad Faisal ◽

Ibrahim M. Almanjahie

Keyword(s):

Time Series ◽

Water Resource Management ◽

River Flow ◽

Time Series Data ◽

Flow Time ◽

Indus Basin ◽

Series Data ◽

Percentage Error ◽

Empirical Bayesian ◽

Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

Download Full-text