The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.
Abstract:Bank Indonesia aims to achieve and maintain rupiah stability where the prices of goods and services are reflected in the development of inflation. The development of inflation must always be considered in determining monetary policy. This study used inflation data from Malang City from January 2012 to May 2019. This time series data was analyzed using the Autoregressive Intergrated Moving Average with Exogenous (ARIMAX) method. The modeling results show that inflation that occurs this month, is influenced by the previous 1 to 4 months. This is due to seasonal effects, and variations in the calendar before Idul Fitri.Abstrak:Bank Indonesia memiliki tujuan untuk mencapai dan memelihara kestabilan nilai rupiah. Kestabilan nilai rupiah adalah kestabilan terhadap harga-harga barang dan jasa yang tercermin dari perkembangan laju inflasi. Perkembangan inflasi harus selalu diperhatikan dalam penentuan kebijakan moneter. Penelitian ini menggunakan data inflasi Kota Malang bulan Januari 2012 hingga Mei 2019. Data time series ini dianalisis dengan metode Autoregressive Intergrated Moving Average with Exogenous (ARIMAX). Hasil pemodelan menunjukkan inflasi yang terjadi pada bukan yang berjalan, dipengaruhi 1 hingga 4 bulan sebelumnya. Hal ini disebabkan efek musiman, dan variasi kalender sebelum hari raya Idul Fitri.
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