The research entitled “Recurrent Neural Network untuk Peramalan Runtun Waktu dengan Pola Long Memory” was conducted by Walid, S.Pd., M.Si. under the guidance of Prof.Drs. Subanar, Ph.D., Prof.Dr.rer.Nat. Dedi Rosadi, M.Si., dan Dr. Suhartono, M.Si. in 2017.
The following is the abstract of this research.
ABSTRACT
In daily practice, modeling of time series was often not only involve the lag or order autoregressive (AR) but also involves a lag or order moving average (MA). This condition occurs in both the linear model which known as the model of autoregressive moving average (ARMA) and the nonlinear models, which is one of its forms is a model of recurrent neural networks (RNN). Feedforward neural networks (FFNN) is one of nonlinear models that can be viewed as a group of highly flexible model that can be used for various applications. Recurrent Neural Network as one of the hybrid models are often used to predict and estimate the issues related to electricity, can be used to describe the cause of the swelling of electrical load which experienced by PLN. In this research will be developed RNN forecasting procedures at the time series with long memory patterns. Considering the application is national electrical load which of course has a different trend with the condition of the electrical load in any country. This research produce the algorithm of time series forecasting which has long memory pattern using FFNN hereinafter referred to the algorithm of fractional integrated feedforward neural networks (FIFFNN). In addition, this research also produce the algorithm of time series forecasting which has long memory pattern using RNN in this case using E-RNN hereinafter referred to the algorithm of integrated fractional recurrent neural networks (FIRNN). The forecasting results of long memory time series using the model of Fractional Integrated Feedforward Neural Network (FIFFNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Feedforward Neural Network (FIFFNN) (24,7,1) provides the smallest MSE value, which is 0.00170185. The forecasting results of long memory time series using models Fractional Integrated Recurrent Neural Network (FIRNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Recurrent Neural Network (FIRNN) (24,6,1) provides the smallest MSE value, which is 0.00149684.
