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

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.

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

2018 ◽  
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.

scholarly journals A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1474 ◽  
Author(s):  
Ming-Chi Tsai ◽  
Ching-Hsue Cheng ◽  
Meei-Ing Tsai
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Sequential Data ◽  
Proposed Model ◽  
Market Indexes ◽  
Fitting Model

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

scholarly journals IMPLEMENTASI METODE FUZZY TIME SERIES DENGAN MODEL ALGORITMA CHEN UNTUK MEMPREDIKSI HARGA EMAS

2020 ◽  
Vol 13 (1) ◽  
pp. 71-78
Author(s):  
Darsono Nababan ◽  
Eric Alexander
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Series Method ◽  
Market Uncertainty ◽  
Low Interest Rates ◽  
Calculation Results ◽  
Time Series Method

Gold is one of the people's preferred forms of investment and is considered the safest (save -heaven). Gold risk which is considered small is the main attraction because in general Indonesian people are not yet familiar with capital market investments such as stocks and mutual funds. But the price of gold is very volatile as for the factors that affect the fluctuations of gold are consumption demand, volatility and market uncertainty, protection of low-interest rates, and the US dollar. Predicting the movement of the gold price and knowing where the direction of the exchange rate moves and determining the price of gold up or down cannot be done accurately and consistently. For this reason, in reducing the risk of loss, an application is needed to predict gold prices using the Fuzzy Time Series Chen algorithm using MATLAB software. In this study to obtain prediction results and comparison charts using actual data and prediction data for the 2015-2017 gold price. From the calculation results obtained by the prediction results with the Fuzzy Time Series method with the Chen algorithm where the average difference between the actual data and prediction data is not more than Rp. 2,850, - where predictions using the Fuzzy Time Series method Chen's algorithm is sufficient to use 1 data to predict the second data which makes this method accurate in predicting the price of gold.

A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

2019 ◽  
Vol 24 (11) ◽  
pp. 8243-8252 ◽  
Author(s):  
Cem Kocak ◽  
Ali Zafer Dalar ◽  
Ozge Cagcag Yolcu ◽  
Eren Bas ◽  
Erol Egrioglu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Time Series Method ◽  
Artificial Neural

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

2021 ◽  
Vol 6 (4) ◽  
pp. 80-89
Author(s):  
Maizatul Akhmar Jafridin ◽  
Nur Fatihah Fauzi ◽  
Rohana Alias ◽  
Huda Zuhrah Ab Halim ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Demand Forecasting ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Strategic Decisions ◽  
Forecasting Performance ◽  
Future Events ◽  
Time Series Method

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.

Sign in / Sign up

Export Citation Format

Share Document