Forecasting is a part of statistical modelling that is widely used in various fields because of its benefits in decision-making. The purpose of forecasting is to predict the future values of certain variables that vary with time using its previous values. Forecasting is related to the formation of models and methods that can be used to produce a good forecast. This research is a survey paper research that used a systematic mapping study and systematic literature review. Generally, time series research uses linear time series models, specifically the autoregressive integrated moving average model that has long been used because it has good forecasting accuracy. The successfulness of the Box–Jenkins methodology is based on the reality that various models can imitate the behaviour of various types of series, usually without requiring many parameters to be estimated in the final choice of the model. However, the assumption of stationarity that must be met makes this method inflexible to use. With the advances in computers, forecasting methods from stochastic models to soft computing continue to develop and extend. Soft computing for forecasting can provide more accurate results than traditional methods. Moreover, soft computing has many advantages in terms of the amount of data that can be analysed and the time- and cost-effectiveness of the process.
Theoretically, the movement of the Composite Stock Price Index (CSPI) is in line with the company’s stock price movements. Hence, it would be appropriate to measure the CSPI contribution to the company’s stock price regarding modeling the company’s stock price. 2-dimensional Geometric Brownian Motion is believed to be the most appropriate model in this case. Therefore, this paper aims to project the share price of PT Telekomunikasi Indonesia in 2018 by considering the CSPI movement. The resulted mean absolute percentage error (MAPE) calculations lead to a conclusion that Prediction with 2-dimensional Geometric Brownian Motion (GBM) is more accurate than the individual modeling of stock prices of PT Telekomunikasi Indonesia. PT Telekomunikasi Indonesia Tbk’s stock price modeling is more appropriately-used by taking heed on the movement of the Composite Stock Price Index. It is conclusive that the two dimensional geometric Brownian motion model provides an accurate prediction of PT Telekomunikasi Indonesia Tbk shares with MAPE is 1.980296%.
Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian food. For increasing production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. The smallest RMSE and AIC values measure model accuracy. The results show that the best model for modeling red onion production in Central Java is obtained by hybrid ARIMA - ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2
on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. It shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.
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