Authors :
Salang Musikasuwan, Tri Wijayanti Septiarini
Volume/Issue :
Volume 5 - 2020, Issue 3 - March
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://bit.ly/2wQZBdO
Abstract :
The main objective of this research is to
propose forecasting model of stock exchange (IDX)
composite index using a weighted fuzzy time series
(WFTS) model. The Mamdani inference system has been
applied with the fuzzy model by using centroid
defuzzification. After the models have been executed and
verified, the performance of WFTS model has been
compared with the conventional fuzzy time series (FTS)
model using root mean square error (RMSE). The results
showed that WFTS models had better performance than
the conventional FTS models. The RMSE values achieved
from WFTS and FTS models for training data sets were
0.314 and 0.4443, and for testing data 0.3246 and 0.4351,
respectively. Finally, it is recommended that optimization
techniques should be employed with the proposed type of
models to improve their performance.
Keywords :
Forecasting Model; Fuzzy Time Series; Weighted Fuzzy Time Series; Stock Exchange Composite.
The main objective of this research is to
propose forecasting model of stock exchange (IDX)
composite index using a weighted fuzzy time series
(WFTS) model. The Mamdani inference system has been
applied with the fuzzy model by using centroid
defuzzification. After the models have been executed and
verified, the performance of WFTS model has been
compared with the conventional fuzzy time series (FTS)
model using root mean square error (RMSE). The results
showed that WFTS models had better performance than
the conventional FTS models. The RMSE values achieved
from WFTS and FTS models for training data sets were
0.314 and 0.4443, and for testing data 0.3246 and 0.4351,
respectively. Finally, it is recommended that optimization
techniques should be employed with the proposed type of
models to improve their performance.
Keywords :
Forecasting Model; Fuzzy Time Series; Weighted Fuzzy Time Series; Stock Exchange Composite.
