An approach for passengers forecasting using Fuzzy Time Series

被引：0

作者：

Syaripudin, U. ^{[1
]}

Zulfikar, W. B. ^{[2
]}

Uriawan, W. ^{[3
]}

Nugraha, R. A. ^{[2
]}

机构：

[1] Asia E Univ, Dept ICT, Kuala Lumpur, Malaysia

[2] UIN Sunan Gunung Djati Bandung, Dept Informat, Bandung, Indonesia

[3] LIRIS INSA Lyon, Lyon, France

来源：

5TH ANNUAL APPLIED SCIENCE AND ENGINEERING CONFERENCE (AASEC 2020) | 2021年 / 1098卷

关键词：

D O I：

10.1088/1757-899X/1098/3/032053

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

At certain times the number of passengers is very difficult to predict. There was a time when the number of passengers occurred a very significant surge. However, there are times when the number of passengers is drastically reduced. This is caused by many factors including time including the holiday season, holidays, and so on. As for other factors such as natural disasters, endemic diseases, and others. The number of passengers must be proportional to the number of vehicles that have been prepared. The company must consider the condition of the vehicle and also the physical condition of the driver. The aim of this study is to conduct passenger forecasting on a coming day. The methodology used is Fuzzy Time Series. The result of the experiment shows that this model has the accuracy of the difference between predictions with real data using PE which is equal to 34.6%.

引用

页数：6

共 50 条

[1] Fuzzy Time Series Forecasting Approach using LSTM Model
Pattanayak, Radha Mohan
Sangameswar, M., V
Vodnala, Deepika
Das, Himansu
[J]. COMPUTACION Y SISTEMAS, 2022, 26 (01): : 485 - 492
[2] Intuitionistic fuzzy time series functions approach for time series forecasting
Eren Bas
Ufuk Yolcu
Erol Egrioglu
[J]. Granular Computing, 2021, 6 : 619 - 629
[3] Intuitionistic fuzzy time series functions approach for time series forecasting
Bas, Eren
Yolcu, Ufuk
Egrioglu, Erol
[J]. GRANULAR COMPUTING, 2021, 6 (03) : 619 - 629
[4] Time series forecasting using fuzzy techniques
Afanasieva, T.
Yarushkina, N.
Toneryan, M.
Zavarzin, D.
Sapunkov, A.
Sibirev, I.
[J]. PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY, 2015, 89 : 1068 - 1075
[5] A Study on Demand Forecasting for KTX Passengers by using Time Series Models
Kim, In-Joo
Sohn, Hueng-goo
Kim, Sahm
[J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2014, 27 (07) : 1257 - 1268
[6] Intuitionistic Fuzzy Time Series: An Approach for Handling Nondeterminism in Time Series Forecasting
Kumar, Sanjay
Gangwar, Sukhdev Singh
[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2016, 24 (06) : 1270 - 1281
[7] A Fuzzy-LP Approach in Time Series Forecasting
Singh, Pritpal
Dhiman, Gaurav
[J]. PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PREMI 2017, 2017, 10597 : 243 - 253
[8] A different approach on fuzzy time series forecasting model
Vamitha, V.
[J]. MATERIALS TODAY-PROCEEDINGS, 2021, 37 : 125 - 128
[9] Handling forecasting problems using fuzzy time series
Hwang, JR
Chen, SM
Lee, CH
[J]. FUZZY SETS AND SYSTEMS, 1998, 100 (1-3) : 217 - 228
[10] Electricity Consumption Forecasting Using Fuzzy Time Series
Bolturk, E.
Oztaysi, B.
Sari, I. U.
[J]. 13TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI 2012), 2012, : 245 - 249

← 1 2 3 4 5 →